should i do a phd machine learning

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Machine Learning Career: Pros and Cons of Having a PhD

• September 25, 2021 at 4:30 pm November 28, 2022 at 12:02 pm

It is often said that data science jobs are for seasoned professionals, and many job ads still show a preference for a profile with a PhD, with years of experience. Yet, many corporate employers have been disillusioned about the value that a PhD brings to the company. Likewise, many professionals, especially among those who just completed a PhD and were offered their first job, find the work sometimes unrewarding.

A PhD may command a slightly higher salary initially, and may be required for a position in a research lab (whether private or government-operated). But for many positions, it may not bring an advantage. Corporate work can be mundane and fast-paced, and the search for perfect algorithms is discouraged, as it hurts ROI. In many companies, a solution close to 80% of perfection is good enough, and requires far less time than reaching 99% perfection, especially since the machine learning models employed are just an approximation of the reality. People with a PhD are not well prepared for that.

Here are some of the negative aspects.

• Even if you pay someone to write your PhD thesis (such services exist), you may spend several years of your life working on your PhD, possibly in a stressful environment, with low pay, delaying buying a home, or getting married. Meanwhile, you see your non-PhD friends ahead of you in their personal life. If you married when working on your PhD, this could eliminate some of these problems.
• Some recruiters may say that you are over-qualified, that your experience is not really relevant to the job you are applying for (or too specialized), and that adapting to a fast-paced corporate environment might be challenging.
• If you land a job in the corporate world, you might find it menial or boring. You could be disappointed that the research you did during your PhD years is a thing of the past, not leading to anything else. This is especially true if your hope was to get a tenured position in the academia, but can’t get one despite your very strong credentials, due to the fierce competition. It can bring long-lasting regrets and nostalgia.
• You may be lacking some coding skills (SQL in particular), which put you at a disadvantage against a candidate with an applied master. Of course, it is always possible and desirable to gain these skills on your own (or via data camps) when working on your PhD.
• Your salary might not be higher than that of a younger candidate with a master degree and the right experience. Your cumulative wealth over your lifetime may be lower.
• Some employers (Google, Facebook, Microsoft, Wall Street,  or defense-related companies) routinely hire PhD’s to work on truly exciting projects. Some only hire from top universities and if your PhD was not from an ivy-league,  you will be by-passed. That said, there are plenty of companies that will hire non ivy-league candidates, and I think that’s a smart move. After all, I earned my PhD in some unknown university, and eventually succeeded in the corporate world.

For some, the pros outweigh the cons by a long shot. This was my case. I provide a few examples below.

• If your PhD was very applied in a hot field (in my case in 1993, processing digital satellite images for pattern detection), you learned how to code, played with a lot of messy data, and even got part-time job in the corporate world, related to your thesis when working on it, then you are up to a good start. In my case, solid funding for the research, and even data sets, came from governmental agencies (EU and others) and private companies (Total, for instance) trying to solve real problems. This adds credibility to your PhD experience. On the downside, my mentor was not a great scholar, but a good salesman able to attract many well paid contracts.
• If you earned your PhD abroad like I did, it is quite possible that you were paid better than your peers in US. In my case, my salary, as a teaching assistant, was similar to that of a high school teacher. And conference attendance (worldwide) was paid by the university or by the agencies that invited me as a speaker. Coming from abroad is sometimes perceived as an advantage, due to showing cultural adaptation, and in most cases, being multilingual and able to easily relocate in various locations if corporate needs ask for it.
• You can still continue to do your research, decades after leaving academia. I still write papers and books to this day. The level is even higher than during my PhD years, but the style and audience is very different, as I try to present advanced results, written in simple English, to a much larger audience. I find this more rewarding than publishing in scientific journals, read by very few, and obfuscated in jargon.
• There are great positions in many research labs, private or government, available only to PhD applicants. The salary can be very competitive.
• VC funding is usually contingent to having a well-known PhD scientist on staff, for startup companies. So if you create your own startup, or work for one, a PhD is definitely an advantage. Even when I started my own, self-funded publishing / media company (acquired by Tech Target in 2020, and focusing on machine learning), my wife keeps reminding me that I would have had considerably less success without my education, even though you don’t legally need any degree or license to operate this kind of business.

Conclusions

Having a PhD can definitely offer a strong advantage. It depends on the subject of your thesis, where you earned your PhD, and if you worked on real-life problems relevant to the business world. More theoretical PhD’s can still find attractive jobs in various research labs, private or government. The experience may be more rewarding, and probably less political, than a tenured position in academia. It goes both ways: it is not unusual for someone with a pure corporate / business background, to make a late career move to academia, sometimes in a business-related department. Or combining both: academia and corporate positions at the same time.

I wrote an article in 2018, about how to improve PhD programs to allow for an easy  transition to the business world. I called it a doctorship program, and you can read about it  here . I will conclude by saying that another PhD scientist, who earned his PhD in the same unknown math department as me at the same time (in Belgium), ended up becoming an executive at Yahoo, after a short stint (post-doc) at the MIT, working on transportation problems. His name is Didier Burton. Another one (Michel Bierlaire), same year, same math department, also with a short post-doc stint at MIT (mine was at Cambridge University), never got a corporate job, but he is now an happy full professor at EPFL. Also, a Data Science Central intern (reporting to me), originally from Cuba and with very strong academic credentials (PhD, Columbia University, EPFL) got his first corporate job after his internship with us (I strongly recommended him). Despite a mixed academic background in physics and biology, he is now chief data scientist of a private company. His name is Livan Alonso.

About the Author

Vincent Granville is a pioneering data scientist and machine learning expert, founder of  MLTechniques.com  and co-founder of  Data Science Central  (acquired by  TechTarget in 2020), former VC-funded executive, author and patent owner. Vincent’s past corporate experience includes Visa, Wells Fargo, eBay, NBC, Microsoft, CNET, InfoSpace. Vincent is also a former post-doc at Cambridge University, and the National Institute of Statistical Sciences (NISS).

Vincent published in  Journal of Number Theory ,  Journal of the Royal Statistical Society  (Series B), and  IEEE Transactions on Pattern Analysis and Machine Intelligence . He is also the author of multiple books, available  here . He lives  in Washington state, and enjoys doing research on stochastic processes, dynamical systems, experimental math and probabilistic number theory.

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• Doing a PhD in Artificial Intelligence

Artificial intelligence and intelligent systems are thought to be the key to the next ‘industrial revolution’. In a data-rich world, developing an artificial intelligence which can learn from its experience and call on human behaviour to make decisions could change the way we live and offer endless economic, social and scientific applications. Nationwide, there is an increasing demand for AI workers, as the world is becoming more reliant on developing technology and automated systems. Consequently, more and more people are pursuing postgraduate research in AI.

What Does a PhD in Artificial Intelligence Focus On?

A PhD in artificial intelligence will give you a deep understanding of AI, allow you to contribute to the development of emerging technology, and equip you with highly applicable technical skills. For example, engineering applications of artificial intelligence include automation of tasks and parametric modelling. Medical applications include using data-science approaches to identify patterns of illness in clinical data. Financial applications include using machine learning platforms to crunch huge amounts of data and help credit lenders in analysing risk and assess potential borrowers.

Artificial Intelligence PhD programme can focus on:

• Machine learning
• Deep learning
• Natural language processing
• Deep neural network architecture
• Human-machine interaction
• Augmented reality
• And countless other areas

Browse PhDs in Artificial Intelligence

Application of artificial intelligence to multiphysics problems in materials design, from text to tech: shaping the future of physics-based simulations with ai-driven generative models, study of the human-vehicle interactions by a high-end dynamic driving simulator, coventry university postgraduate research studentships, discovery of solid state electrolytes using deep learning, entry requirements for a phd in artificial intelligence.

The entry requirements for a PhD in AI are typically an upper second-class honours degree (or international equivalent) in a relevant subject from an accredited university. Subjects considered relevant to artificial intelligence include computer science, engineering, mathematics, statistics, electronics/electrical engineering or science.

Some research courses also require applicants to possess experience in programming, the desired programming language will be specific to the research project. Academic or work experience in machine learning or data science are typically favourable for applications.

International students will also need to meet several minimum English language requirements set by the university, usually as part of a TOEFL or IELTS exam.

Duration and Programme Types

Like most PhDs, a doctoral programme in Artificial Intelligence typically takes 3-4 years full-time, or 6 years part-time .

Aside from the traditional PhD, there is also the CDT PhD. Many Universities have Centres for Doctoral Training (CDTs) which are often funded by the UKRI centre . These CDTs can offer the CDT PhD which is a specialised PhD programme in artificial intelligence. The main difference between a standard PhD in artificial intelligence and a CDT PhD in artificial intelligence is that the latter includes additional modules which give candidates training in neuroscience, entrepreneurship, high performance computing, AI ethics and science communication.

Due to the different research areas you can pursue within the artificial intelligence field, the nature of programmes can vary. Some PhD research programmes are computational based and heavily reliant on coding, mathematics and lab work. Other research programmes can be people facing, involving questionnaires, for example to determine public perception on proposed legislation.

Costs and Funding

Annual tuition fees for PhDs in Artificial Intelligence are typically around £4,000 to £5,000 for UK/EU students. Tuition fees for international students are usually much higher, typically around £25,000 per academic year.

A variety of scholarships and funding support options are available for postgraduate study. For Artificial Intelligence in particular, the UKRI and ESPRC offer a number of studentships and CDT opportunities across varies universities. Many universities have research centres which are partnered with UK research councils and offer fully funded programmes. Funding is generally available for UK/EU students. International students are also eligible for some funding opportunities, but these tend to be less widely available.

Available Career Paths in AI

One of the key advantages of Artificial Intelligence is that it has a wide range of applications, and hence there are many career paths available. As computer systems and data have become more integrated in everyday life, the demand for experts in AI has grown rapidly. This high demand has resulted in many high job security and lucrative salaries.

Examples destinations for an AI PhD student include:

Data Analyst – If you are very analytical research student, you may use your artificial intelligence PhD to pursue a career in data science or analysis. Data analysts can be found in engineering, finance, healthcare, and everywhere in between. They are responsible for data crunching and using their skills to present complex information in a clear manner – visually and orally. Typical duties include record management, maintaining automated processes, monitoring analytics and KPIs, improving algorithms, and creating dashboards for clients. The average salary for data analysts in the UK is around £30,000 – £45,000, though this number can increase drastically depending on the sector.

Cyber Security – As cyber-attacks are becoming more commonplace, industries are looking to develop their cyber security, and salaries are seeing a sharp increase accordingly. AI doctorates are well placed for a career in cyber security, and typical career destinations include security analysts, penetration testers, systems engineers, web developers and cybersecurity consultants. In these roles, you will be responsible for protecting IT infrastructure and help develop security systems.

Machine learning – Often those with a PhD in AI become machine learning engineers, responsible for the development of intelligent systems. Machine learning is a subset of AI which focuses on the idea that machines can be programmed to learn from data and experience to improve decision making without human input. Machine learning is perhaps at the forefront of AI research, and there are numerous programmes look to improve its capabilities. This is well suited for those who enjoy the mathematical and programming side of computer science. Typical duties include managing data pipelines, developing algorithms, liaising with stakeholders, analysing datasets, and leading software design. Entry level salaries are around £35,000 and can exceed £150,000 with experience. Deep learning is similar to machine learning, the main difference is that deep learning aims to create artificial ‘neural networks’.

Postgraduate research often leads to an academic career. As an academic you can propose your own research projects based on your interest and supervise students. As a professor you can shape the next generation of AI experts and as a researcher you can make use of a university’s department resources, facilities and industrial ties to work with cutting edge technology and push the boundaries of our knowledge.

Browse PhDs Now

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Machine Learning (Ph.D.)

The curriculum for the PhD in Machine Learning is truly multidisciplinary, containing courses taught in eight schools across three colleges at Georgia Tech: the Schools of Computational Science and Engineering, Computer Science, and Interactive Computing in the College of Computing; the Schools of Industrial and Systems Engineering, Electrical and Computer Engineering, and Biomedical Engineering in the College of Engineering; and the School of Mathematics in the College of Science.

Reflections on my (Machine Learning) PhD Journey

December 31, 2020

2020 has been an incredibly challenging year, and on a personal note, has marked an important milestone — graduating with my PhD in computer science from Cornell University. This has been a six year journey, where my personal growth as a machine learning researcher (from thrills of first discoveries to the laborious grind through publication rejections to identifying a broader research vision) also took place against the backdrop of the rapid growth and change of the entire field ( 2014 NeurIPS : ~2k attendees, 2020 NeurIPS : ~20k attendees).

With this year coming to an end, I’ve put together some of my reflections and lessons learned from my (Machine Learning) PhD experiences. I discuss topics including expectations going in, common challenges during the PhD (and some strategies for helping with them), keeping up with papers, the community nature of research and developing a research vision. I hope that these topics are helpful in navigating the PhD and research in Machine Learning!

Expectations going into the PhD

Feeling completely stuck, feeling overwhelmed with keeping up with ml progress, feeling isolated, three useful personal skills, keeping notes on papers and ideas, the importance of community, developing a research vision.

In the post title, I’ve referred to the PhD as a “journey”, an aspect often underappreciated, particularly if one is coming straight out of undergrad (which was my experience). A typical Machine Learning PhD is going to be ~5-6 years of relatively unstructured time, and during this, not only will you learn research skills and knowledge about the field, but you’ll also develop personal preferences on how interesting specific problems are, the aesthetics of different subfields, and even perspectives on the type of work being done across academia/industry/policy/nonprofits.

These evolving personal preferences will influence the type of research you decide to work on, and even the post-PhD career path you pick. But particularly at the start of the PhD, it’s hard to predict how these personal perspectives will evolve. In my case, I started my PhD fully assuming I’d stay in industry, part way through began seriously considering academia, and at the end made the very difficult decision to turn down academic offers and stay on in industry. So going into the PhD program, it’s often helpful to take it step by step, and focus on getting the most out of the experience (learning/research/community participation), instead of a very specific desired outcome (which is prone to change, and may also add unnecessary pressure.)

Common Challenges through the Journey

Doing a PhD can be an immensely rewarding experience, and, particularly in Machine Learning, offers the chance to contribute to fundamental scientific understanding as well as impactful technology deployment. I’ve been grateful to my PhD for providing many opportunities to experience both of these! However, the duration and unstructured nature of the PhD can also make it challenging. My journey definitely consisted of ups and downs, and at various times I’ve struggled with feeling isolated, completely stuck, and even overwhelmed by trying to keep up with the rapid pace of progress. Looking back, and through discussion with peers, I now know these low points can unfortunately be quite common. But because these experiences are shared across many people, there can also be strategies for working through them. Below I discuss some of these experiences and strategies.

One very common challenge is feeling completely stuck, either on a specific project or with regards to the research process on the whole.

If the challenge is a specific project, where you’ve pushed hard and it’s still not quite working, then some strategies that might help are

• Making a write up : Collect any partial experimental results, mathematical insights, jotted notes on motivation, etc and take time to put together a write up. This can help with providing a picture of where things stand and where the important gaps are.
• Pivot : if there’s a specific part of the project that’s not working, is there the possibility to reframe the question (possibly taking inspiration from related work) to make it more tractable?
• Forming connections : are there links between what the current project focuses on and other areas of study? Can that connection be explored in this project? This can both help progress and in making the project relevant to a broader community.
• Feedback on writeup : It might also be helpful to get feedback on the project write up from peers, collaborators and friends in the research community. They may be able to offer new perspectives or suggest improvements.
• Workshop submission : it can also be useful to make a workshop submission. This also provides a chance to help collect together all the research results and get useful feedback. (For some time now, I’ve gained the most out of the workshops at machine learning conferences, due to being able to discuss/get feedback on ongoing directions and meeting other researchers working on the same area.)
• Wrap up and move on : Occasionally, there may be a project which sounded promising in the beginning, but has been difficult to make work, and is also inherently challenging to reframe or form connections to other areas. In this (difficult) situation, it may make most sense to wrap up the project quickly and move on. If you have partial results, it’s likely worthwhile to create a final writeup of those and share, so one option is to do this, get confirmation from collaborators and final feedback, and keep it as an arXiv preprint or workshop paper.

If the feeling of being stuck originates from the research process more broadly, one important point I’ve realised is that gaining research maturity can often be very hard to measure, especially when evaluating yourself! Midway through my PhD, I started working on some healthcare applications, and was only making slow headway on learning about the area/writing papers. This had me feeling stuck and somewhat frustrated at my slowdown in research progress. But when I re-read some papers that I’d first come across at the start of my PhD, the depth and context with which I could understand their results was strikingly different from earlier on.

Critical aspects of research maturity — understanding the broader context of results, being able to form connections between different areas, quickly narrowing in on novel key contributions in your subfield — don’t immediately translate to tangible outputs (more papers). But they’re central for becoming an independent researcher with a rich research vision — arguably the main research goal of the PhD. And if you’re reading papers, learning about the field, and working on research directions yourself, (and maybe even teaching/mentoring) most likely you’re making progress on all of these important aspects!

Machine Learning is a vibrant, fast-paced field. But the flipside of this is drowning in the flood of new papers, new preprints, new blogposts, new implementations, new frameworks, etc, etc. (Fun statistic: NeurIPS this past year had ~10k submissions and ~2k accepted papers — no wonder we’re feeling overwhelmed!)

My strategy in dealing with this has been

• Have a number of go-to links to find references to related papers. For me, this has been a combination of (i) subscribing to the arxiv stat.ML cs.LG mailing lists, arXiv-sanity , Twitter, (occasionally) reddit/MachineLearning , paperswithcode and perusing Semantic Scholar / Google Scholar .
• Keep a reading list of papers If I come across an interesting paper but don’t have time to read it then (often the case), I make a note of it and try to return to it later.
• Have a paper reading strategy If a paper is very close to research directions I’m actively working on, I’ll read it in detail, otherwise I’ll skim the abstract to get a high level picture.
• Occasionally read up on different areas Occasionally (maybe once per year), I’ll look into a few interesting areas I’m not working on, and read several papers in each to get a sense of what is being worked on.

It’s also helpful to remember that (i) everyone feels overwhelmed with the rate of publishing and (ii) many papers may rely on the same underlying idea, and often being familiar with the idea is enough for keeping up with the field.

Another common challenge in the PhD is struggling with feelings of isolation. In the first couple of years of my PhD, some projects required that I kept laser focus on very narrow, specific questions, which were also highly laborious and (felt) never-ending. During those times, it was hard not to feel completely cut-off from other researchers and the broader field, and I’m very grateful for all the support and guidance from my PhD advisor in pushing through that situation.

More broadly, this scenario can be common especially earlier on during the PhD, where you might simultaneously be learning how to see through a research project from start to finish, and at the same time have less context and connections to the broader research field/community. Maintaining connections to the field/community can be very helpful in making sure you don’t feel isolated. Some ways to do this could be: (i) setting up collaborations with (senior) students/postdocs (ii) getting feedback on your work in progress – this might be your advisor/lab, but could also be other peers/mentors working in the field (iii) actively participating in the broader community, whether that’s through simply attending conferences, mentoring or organizing workshops.

Having discussed some of the common challenges faced in the PhD and ways to help address them, the rest of this post will overview some useful considerations for research progress.

In particular, I’ll begin by discussing three personal skills I found to be very helpful throughout the PhD: initiative, focus and perseverance. This is of course drawing on my personal experiences, and there are varying opinions on useful personal skills! But for me, coming out of undergrad, a key difference I noticed in the PhD program was the need to take initiative — whether that meant reading important relevant papers, doing rapid preliminary studies of the feasibility of different approaches, talking to peers doing related research, or even attending and being an active participant in conferences. Because the PhD broadly consists of unstructured time, being productive largely relies on your initiative for learning and conducting research.

Two other skills that I’ve found very helpful are focus and perseverance. When getting started with a new research direction, focus is very helpful to peruse related work, distill the key points, quickly learn from initial exploration and determine the main project directions. Perseverance on the other hand is very useful (especially) for wrapping up the research project: there’s often a long tail of edits/additions for the paper to be submission ready, and post-submission, further edits to respond to peer-review and paper rejections. It can be hard to muster the motivation to make all of these edits (especially when preparing the paper for yet another resubmission, and having newer, more interesting projects also going on), but the variability of the peer-review process often means it’s worth persevering through.

Through my PhD, there are two documents, one started in my first year, and the other in my third year, that I’ve continuously kept updated. The first keeps track of papers that I’ve read – every time I read a new paper, I add it into the doc, along with a short summary of my takeaways. The document is now over fifty pages (which maybe means I should switch to Mendeley or Paperpile), and has been a very useful way to quickly flick back to papers I’ve read years earlier and get key points. The other document keeps track of research ideas. Everytime I have a promising new idea, I make a note of it. Over time, this has helped inform my research directions and highlight key themes.

One important property of (Machine Learning) research that took me time to realise is that research is fundamentally a community endeavor. The problems that we aim to tackle are incredibly difficult, and progress relies on the cycle of you building off of others’ ideas and others building off of your ideas. This is a crucial factor to keep in mind when exploring research directions. What is the community excited about, and why? Are there shortcomings or gaps? Are there natural next steps to study? Taking the time to discuss these questions and others with peers in the community is vital to developing well-informed, germane research questions. And if you identify an exciting, new research direction of interest to the field, it’s often useful to build a community around that direction — this can happen through initiating collaborations, disseminating key open questions and organizing workshops. From very early on in my PhD, I was interested in understanding the key empirical phenomena exhibited by our modern deep learning systems. But working on this topic then was very challenging. The field was evolving rapidly, making the focus of any kind of analysis a moving target, and significantly adding to the challenge of building a new community around this topic. So publishing my first deep learning analysis papers was pretty difficult, and definitely an act of perseverance! But since then, it’s been wonderful to witness and contribute to the growth of this exciting research area!

While I described earlier that when getting started with the PhD, it may be better to take things one step at a time and focus on the experience instead of a specific goal, from the research maturity perspective, the PhD does have a specific goal: to make you an independent researcher, with a rich (articulable) research vision.

In current Machine Learning research, with the deluge of papers, it’s easy to feel stressed about the need to continuously churn out publications. But while paper writing is an important skill, I think the crucial test of research maturity is being able to have knowledgeable perspectives on your field which help identify key research questions, connected by overarching themes — a research vision.

Having a well developed research vision is enormously motivating. As an analogy, it’s a little like completing a “paint by numbers” kit: instead of just seeing the color of each individual square, you suddenly appreciate the full picture.

So how does one develop a research vision?

As a first note, from my PhD journey, I think it’s hard to develop a full-fledged research vision without some years of research experience. In my first couple of years of PhD, I remember reading papers and watching talks of senior researchers, and being frustrated that I couldn’t identify/articulate interesting research questions nearly as well. In the years since then, the compounding effects of all the papers I’ve read, projects worked on, seminars attended, have significantly improved my ability to do this. (There is of course room to improve! Going forwards, this ability will continue to develop, as I gain more context and understanding of larger subfields.)

Being more specific about the stages that led to a (better developed) research vision: it started off with exploration, with my first few projects giving me diverse exposures and helping me understand what I found intrinsically interesting. From there, there were natural followup projects to study, which finally also led to some related questions on applications/deployment. All of this started coming together under the broad theme of Machine Learning Design and Human-AI interaction at Deployment, and, as research visions are good at doing, also inspired new questions. (I am deeply grateful to my PhD advisor for his insights, guidance and encouragement through all of this!)

As a final point, I want to emphasise that the years of experience really do have a compounding effect. As you work on research projects, it becomes easier to identify the salient ideas in research papers, this informs your personal perspectives and promising questions for next projects, working on these next projects makes it easier to absorb/give talks, which then circles back to help with identifying new interesting research directions, which eventually coalesce to form a broader vision.

In summary, doing a PhD can be very fulfilling. It is however a journey, and has its ups and downs, personal discoveries, and evolution of (research) perspectives. I’m very grateful for the many rich experiences during my PhD, and hope this post might be helpful for others on the journey!

• Internal wiki

PhD Programme in Advanced Machine Learning

The Cambridge Machine Learning Group (MLG) runs a PhD programme in Advanced Machine Learning. The supervisors are Jose Miguel Hernandez-Lobato , Carl Rasmussen , Richard E. Turner , Adrian Weller , Hong Ge and David Krueger . Zoubin Ghahramani is currently on academic leave and not accepting new students at this time.

We encourage applications from outstanding candidates with academic backgrounds in Mathematics, Physics, Computer Science, Engineering and related fields, and a keen interest in doing basic research in machine learning and its scientific applications. There are no additional restrictions on the topic of the PhD, but for further information on our current research areas, please consult our webpages at http://mlg.eng.cam.ac.uk .

The typical duration of the PhD will be four years.

Applicants must formally apply through the Applicant Portal at the University of Cambridge by the deadline, indicating “PhD in Engineering” as the course (supervisor Hernandez-Lobato, Rasmussen, Turner, Weller, Ge and/or Krueger). Applicants who want to apply for University funding need to reply ‘Yes’ to the question ‘Apply for Cambridge Scholarships’. See http://www.admin.cam.ac.uk/students/gradadmissions/prospec/apply/deadlines.html for details. Note that applications will not be complete until all the required material has been uploaded (including reference letters), and we will not be able to see any applications until that happens.

Gates funding applicants (US or other overseas) need to fill out the dedicated Gates Cambridge Scholarships section later on the form which is sent on to the administrators of Gates funding.

Deadline for PhD Application: noon 5 December, 2023

Applications from outstanding individuals may be considered after this time, but applying later may adversely impact your chances for both admission and funding.

FURTHER INFORMATION ABOUT COMPLETING THE ADMISSIONS FORMS:

The Machine Learning Group is based in the Department of Engineering, not Computer Science.

We will assess your application on three criteria:

1 Academic performance (ensure evidence for strong academic achievement, e.g. position in year, awards, etc.) 2 references (clearly your references will need to be strong; they should also mention evidence of excellence as quotes will be drawn from them) 3 research (detail your research experience, especially that which relates to machine learning)

You will also need to put together a research proposal. We do not offer individual support for this. It is part of the application assessment, i.e. ascertaining whether you can write about a research area in a sensible way and pose interesting questions. It is not a commitment to what you will work on during your PhD. Most often PhD topics crystallise over the first year. The research proposal should be about 2 pages long and can be attached to your application (you can indicate that your proposal is attached in the 1500 character count Research Summary box). This aspect of the application does not carry a huge amount of weight so do not spend a large amount of time on it. Please also attach a recent CV to your application too.

INFORMATION ABOUT THE CAMBRIDGE-TUEBINGEN PROGRAMME:

We also offer a small number of PhDs on the Cambridge-Tuebingen programme. This stream is for specific candidates whose research interests are well-matched to both the machine learning group in Cambridge and the MPI for Intelligent Systems in Tuebingen. For more information about the Cambridge-Tuebingen programme and how to apply see here . IMPORTANT: remember to download your application form before you submit so that you can send a copy to the administrators in Tuebingen directly . Note that the application deadline for the Cambridge-Tuebingen programme is noon, 5th December, 2023, CET.

What background do I need?

An ideal background is a top undergraduate or Masters degree in Mathematics, Physics, Computer Science, or Electrical Engineering. You should be both very strong mathematically and have an intuitive and practical grasp of computation. Successful applicants often have research experience in statistical machine learning. Shortlisted applicants are interviewed.

Do you have funding?

There are a number of funding sources at Cambridge University for PhD students, including for international students. All our students receive partial or full funding for the full three years of the PhD. We do not give preference to “self-funded” students. To be eligible for funding it is important to apply early (see https://www.graduate.study.cam.ac.uk/finance/funding – current deadlines are 10 October for US students, and 1 December for others). Also make sure you tick the box on the application saying you wish to be considered for funding!

If you are applying to the Cambridge-Tuebingen programme, note that this source of funding will not be listed as one of the official funding sources, but if you apply to this programme, please tick the other possible sources of funding if you want to maximise your chances of getting funding from Cambridge.

What is my likelihood of being admitted?

Because we receive so many applications, unfortunately we can’t admit many excellent candidates, even some who have funding. Successful applicants tend to be among the very top students at their institution, have very strong mathematics backgrounds, and references, and have some research experience in statistical machine learning.

Do I have to contact one of the faculty members first or can I apply formally directly?

It is not necessary, but if you have doubts about whether your background is suitable for the programme, or if you have questions about the group, you are welcome to contact one of the faculty members directly. Due to their high email volume you may not receive an immediate response but they will endeavour to get back to you as quickly as possible. It is important to make your official application to Graduate Admissions at Cambridge before the funding deadlines, even if you don’t hear back from us; otherwise we may not be able to consider you.

Do you take Masters students, or part-time PhD students?

We generally don’t admit students for a part-time PhD. We also don’t usually admit students just for a pure-research Masters in machine learning , except for specific programs such as the Churchill and Marshall scholarships. However, please do note that we run a one-year taught Master’s Programme: The MPhil in Machine Learning, and Machine Intelligence . You are welcome to apply directly to this.

What Department / course should I indicate on my application form?

This machine learning group is in the Department of Engineering. The degree you would be applying for is a PhD in Engineering (not Computer Science or Statistics).

How long does a PhD take?

A typical PhD from our group takes 3-4 years. The first year requires students to pass some courses and submit a first-year research report. Students must submit their PhD before the 4th year.

What research topics do you have projects on?

We don’t generally pre-specify projects for students. We prefer to find a research area that suits the student. For a sample of our research, you can check group members’ personal pages or our research publications page.

What are the career prospects for PhD students from your group?

Students and postdocs from the group have moved on to excellent positions both in academia and industry. Have a look at our list of recent alumni on the Machine Learning group webpage . Research expertise in machine learning is in very high demand these days.

Machine Learning - CMU

Requirements for the phd in machine learning.

• Completion of required courses , (6 Core Courses + 1 Elective)
• Mastery of proficiencies in Teaching and Presentation skills.
• Successful defense of a Ph.D. thesis.

Teaching Ph.D. students are required to serve as Teaching Assistants for two semesters in Machine Learning courses (10-xxx), beginning in their second year. This fulfills their Teaching Skills requirement.

Conference Presentation Skills During their second or third year, Ph.D. students must give a talk at least 30 minutes long, and invite members of the Speaking Skills committee to attend and evaluate it.

Research It is expected that all Ph.D. students engage in active research from their first semester. Moreover, advisor selection occurs in the first month of entering the Ph.D. program, with the option to change at a later time. Roughly half of a student's time should be allocated to research and lab work, and half to courses until these are completed.

Master of Science in Machine Learning Research - along the way to your PhD Degree.

Other Requirements In addition, students must follow all university policies and procedures .

Rules for the MLD PhD Thesis Committee (applicable to all ML PhDs): The committee should be assembled by the student and their advisor, and approved by the PhD Program Director(s).  It must include:

• At least one MLD Core Faculty member
• At least one additional MLD Core or Affiliated Faculty member
• At least one External Member, usually meaning external to CMU
• A total of at least four members, including the advisor who is the committee chair

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Best Online Doctorates in Machine Learning: Top PhD Programs, Career Paths, and Salary

Machine learning is a rapidly growing, fascinating field dealing with algorithm development that can be used to make predictions from data. The best online PhD in Machine Learning prepares students for a career in this promising field.

The best online doctorates in machine learning offer students a comprehensive education in all aspects of the field. Students are also provided with the opportunity to choose a specialization such as deep learning, natural language processing , or computer vision. Find out in this article what machine learning PhD online degree program best fits you and the machine learning jobs for graduates.

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Can you get a phd in machine learning online.

Yes, you can get a PhD in Machine Learning online. The online learning system has seen rapid growth in many academic fields and has given students the opportunity to virtually access the academic curriculum remotely.

Many online PhD programs in the United States are accredited and designed with working professionals in mind. Online learning is a great way to earn a doctorate without sacrificing your day job, and in most cases, online students can complete their entire academic journey without stepping foot on campus.

Is an Online PhD Respected?

Yes, an online PhD is respected when it is obtained from an accredited institution in the US. A PhD from an unaccredited school is regarded as just an expensive piece of paper by many other academic institutions.

In regard to employment, many companies and organizations respect an online PhD, holding it to the same standard as an in-person PhD. However, some employers prefer in-person degrees and will disregard online degrees. Ensure your potential future employer accepts online degrees as credible education.

What Is the Best Online PhD Program in Machine Learning?

The best online PhD program in machine learning is at Clarkson University in Potsdam, New York. It is regionally accredited by the Middle States Commission on Higher Education and has an excellent reputation within the academic community, a student-to-faculty ratio of 12 to one, and one in five of its 44,000 alumni is a CEO or executive.

Why Clarkson University Has the Best Online PhD Program in Machine Learning

Clarkson University has the best machine learning PhD program not only because it is accredited by the Middle States Commission on Higher Education (MSCHE) but also because of its US News & World Report ranking. MSCHE is a regionally recognized accreditation association that uses a rigorous and comprehensive system for the purpose of accreditation.

Referring to US News & World Report, Clarkson University is ranked 127 for best national universities out of 4000 degree-granting academic institutions in the United States and 49 for best value schools.

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Online PhD in Machine Learning Admission Requirements

The admission requirements for an online PhD in Machine Learning typically include a master’s degree or Bachelor’s in Machine Learning or a related subject like the field of engineering. Moreover, prepare to submit official transcripts from previously attended postsecondary institutions and GRE test scores.

Additionally, you may be asked to submit three letters of recommendation, a statement of purpose, a CV or resume, and prove your knowledge of calculus and your fluency in computer programming languages like Python and Java. Below is a list of the typical admission requirements needed by distinct schools that offer a machine learning PhD program.

• Master’s or bachelor’s degree in a relevant field
• Official transcripts and GRE test scores
• Letters of recommendation
• Statement of purpose
• CV or resume
• Knowledge of programming and calculus

Best Online PhDs in Machine Learning: Top Degree Program Details

Best online phds in machine learning: top university programs to get a phd in machine learning online.

The top university programs to get a PhD in Machine Learning are at Clarkson University, Aspen University, Capitol Technology University, The University of Rhode Island, and The University of the Cumberlands, among other distinct schools.

This section discusses the properties, requirements, and descriptions of the best universities offering online PhD in Machine Learning programs. We have created this list below to narrow down your school search for these graduate-level in-depth study programs.

Aspen University is a Distance Education Accrediting Commission accredited university. It was established in 1987 as a private for-profit online university offering undergraduate and graduate degrees in computer science, business information systems, and project management.

Aspen University in Phoenix, Arizona is a known member of the Council for Adult and Experiential Learning and is dedicated to supporting adult learners in achieving a professional career in whatever field they desire.

DSc in Computer Science

This doctoral degree teaches students the theory and practical application of computer science in data science, application design, and computer architecture. It contains 20 courses, including artificial intelligence, risk analysis, and system metrics. 

These courses are offered online and aim to impart students with the necessary skills for improving existing technology, as well as evaluating and applying them. It also contains courses that aid doctoral students in carrying out their research dissertations.

DSc in Computer Science Overview

• Accreditation: Distance Education Accrediting Commission
• Program Length: 5 years and 7 months
• Acceptance Rate: N/A
• Tuition and Fees: $375/month

DSc in Computer Science Admission Requirements

• Master’s degree
• Statement of goals
• Minimum of 3.0 GPA
• Must know about object-oriented development

Capitol Technology University was founded in 1927 and offers online programs at the undergraduate, graduate, and doctoral levels. The areas of study in which these online programs are offered include business, technology, and the field of engineering.

PhD in Artificial Intelligence

This is a research-based PhD program that offers students the opportunity to conduct research in any field of their choice. Throughout the program, student work must be approved by the academic supervisor. Students are to submit a thesis and give an oral presentation which will be supervised by an expert in the field.

PhD in Artificial Intelligence Overview

• Accreditation: Middle States Commission on Higher Education
• Program Length: 2 to 3 years
• Tuition and Fees: $933/credit

PhD in Artificial Intelligence Admission Requirements

• Application fee of $100
• Master’s degree in a relevant field
• Minimum of five years of related work experience
• Two recommendation letters

Founded in 1973, City University of Seattle is recognized as a top 10 educator of adults nationwide, as ranked by the US News & World Report for school rankings. It offers online undergraduate, graduate, and doctoral programs designed for working professionals

PhD in Information Technology

The program’s curriculum consists of courses in machine and deep learning. Candidates are given the option to propose their depth of study, which requires approval from the academic director. The program consists of core courses, concentration courses, a comprehensive examination, a research core, and a dissertation. 

PhD in Information Technology Overview

• Accreditation: Northwest Commission on Colleges and Universities
• Program Length: Flexible
• Acceptance Rate: 100% due to open admission policy
• Tuition and Fees: $765/credit

PhD in Information Technology Admission Requirements

• A master’s degree from an accredited or recognized institution
• CV and resume, and three references letters 
• Proof of English proficiency
• Interview with admissions advisor
• State goals related to your academic work

Founded in 1896 to honor Thomas S. Clarkson, Clarkson University offers flexible online degree programs at the undergraduate and graduate levels. It is a research university that leads in technology education. 

PhD in Computer Science

This doctoral program places emphasis on areas such as artificial intelligence , software, security, and networking. Current students are required to complete 36 credits of computer science foundation and research-oriented courses, elective courses, achieve candidacy within the first two years of the program, and propose and defend a thesis.

PhD in Computer Science Overview

• Program Length: 3 years
• Tuition and Fees: $1,533/credit

PhD in Computer Science Admission Requirements

• Complete the online application form
• Resume, statement of purpose, and three letters of recommendation
• English proficiency test for international applicants (TOEFL, IELTS, PTE, and Duolingo English Test)

Northcentral University is a private university established in 1996 and is designed for flexibility by offering programs of distance learning for working professionals. It practices a distinctive one-to-one learning system and has a dedicated doctoral faculty.

In this doctorate program, besides writing papers about past research, students are allowed to propose their research. Its curriculum consists of subjects such as software engineering , artificial intelligence, data mining, and cyber security. Through the course, students conduct research and examine real-world issues in the field of computer science.

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• Accreditation: WASC Senior College and University Commission
• Program Length: 3 years and 4 months
• Tuition and Fees: $1,094/credit
• Master’s degree from an accredited institution
• Official transcripts
• English proficiency exam score for international students

Nova Southeastern University was founded in 1964 in Fort Lauderdale, Florida. It offers online graduate and undergraduate courses and conducts a wide variety of interdisciplinary healthcare research. It is home to national athletics champions and Olympians.

This program provides research in computer science. Its format of learning combines both traditional and online instruction designed with consideration for working professionals . Its coursework consists of research in computer science areas, including cyber security, software engineering, and artificial intelligence.

• Accreditation: Southern Association of Colleges and Schools, Commission on Colleges
• Program Length: Not specified
• Tuition and Fees: $1,282/credit
• Online application and $50 application fee
• A bachelor’s or master’s degree in a relevant field from a regionally accredited institution
• GPA of at least 3.20 
• Official transcripts from all institutions attended 
• A resume  
• Essay, and three letters of recommendation

The University of North Dakota was founded in 1883, six years before North Dakota was made a state. Today, it offers several academic programs in undergraduate, graduate, and doctoral fields and is known for conducting research in areas that include medicine, aerospace, and engineering.

This PhD in Computer Science curriculum consists of courses in machine learning, software engineering, applications of AI, computer forensics, and computer networks which benefit students by granting them proficiencies in areas such as artificial intelligence, compiler design, operating systems, simulation, databases, and networks.

• Accreditation: Higher Learning Commission
• Program Length: 4 to 5 years
• Tuition and Fees: $545.16/credit (in state); $817.73/ credit (out of state)
• Application fee of $35
• Master’s or bachelor’s degree in engineering or a related science field
• GPA of 3.0 on a 4.0 scale and GRE test score
• Official copy of all college and university academic transcripts
• Statement of academic goals and three letters of recommendation
• Expertise in a high-level programming language and basic knowledge of data structures, formal languages, computer architecture and OS, calculus, statistics, and linear algebra 
• English language proficiency

The University of Rhode Island is a public research institution founded in 1892. It conducts extensive research in the field of science. It offers online, on-site, and hybrid programs at the graduate and undergraduate levels, as well as certificate programs.

In this PhD in Computer Science program, students are involved in research geared toward producing new intellectual and innovative contributions to the field of computer science. It offers a combination of on-campus, online, and day and evening classes. It consists of courses in machine learning, artificial intelligence, software engineering, and systems simulation.

• Accreditation: New England Commission of Higher Education
• Program Length: 4 years
• Tuition and Fees: $14,454/year (in-state); $27,906/ year (out of state)
• An undergraduate degree from a regionally accredited institution in the US
• A minimum GPA of 3.0
• All official college transcripts
• Personal statement
• An application fee of $65

Founded in 1888 by Baptist ministers in Williamsburg KY, today the University of the Cumberlands offers online master's and doctoral degree programs in the fields of education, information technology, and business.

The program requires 18 credit hours of core courses which include information technology geared toward creating machine learning engineers . Its curriculum focuses on predictive analytics and other skills students need to become experts in cyber crime security, big data, and smart technologies.

Students have the option to specialize in information systems security, information technology, digital forensics, or blockchain technologies. Students will complete 21 credit hours of professional research while working toward a dissertation.

• Tuition and Fees: $500/credit
• A master’s degree from a regionally accredited institution
• TOEFL for non-native English speakers
• Application fee of $30

Wright State University was first seen in 1964 as a branch campus for Ohio State University and Miami University. It is a Carnegie classified research university and offers research at the undergraduate, graduate, and doctoral levels.

PhD in Computer Science and Engineering

This degree is awarded to students who show excellence in study and research that significantly contributes to the field of computer science and engineering. The degree requirements include an A grade completion of the core coursework in two areas and at least a B in the third. 

Students are to complete a minimum of 18 hours of residency research before taking the candidacy exam, which must be completed with a satisfactory grade. Also, a minimum of 12 hours of dissertation research is needed before the dissertation defense, which has to be approved.

PhD in Computer Science and Engineering Overview

• Program Length: 10 years time limit
• Tuition and Fees: $660/credit (in state); $1,125/ credit (out of state)
• Bachelor’s or master’s degree in a related discipline (computer science or engineering)
• Minimum GPA of 3.0 if admitted with a bachelor’s degree or 3.3 with a master’s degree
• GRE general test portion
• TOEFL score for non-native English speakers
• Knowledge of high-level programming languages, computer organization, operating systems, data structures, and computer systems design
• A record that indicates potential for a career in research

Online Machine Learning PhD Graduation Rates: How Hard Is It to Complete an Online PhD Program in Machine Learning?

It is very hard to complete an online PhD in Machine Learning. According to a paper published in the International Journal of Doctoral Studies, there is a PhD attrition rate of 50 percent in the US within the past 50 years. Therefore, the graduation rate for doctorate students is approximately 50 percent.

How Long Does It Take to Get a PhD in Machine Learning Online?

It takes about four years to get a PhD in Machine Learning online, which is fast when compared to a traditional in-person PhD program which may take over seven years to complete. Online PhD programs are accelerated by default, so the curriculum focuses on the major needs of a PhD graduate in the areas of research, thesis, and dissertation.

Students may be able to reduce the time spent pursuing a PhD in Machine Learning by first acquiring a master’s degree in the field. If you choose to pursue a PhD on a part-time schedule as opposed to full-time study, it will significantly increase the time it takes to acquire the degree.

How Hard Is an Online Doctorate in Machine Learning?

Getting an online doctorate in machine learning is very hard, as are most graduate programs. Besides the rigorous research, strict requirements, deadlines, qualification examinations, and dissertations, other challenges may exist, such as limited student connection with the faculty members, isolation, financial issues, and lack of an adequate work-life balance .

Getting a doctorate in any field is not easy. In fact, there is research to suggest that online doctorate students face challenges regarding culture and academia. As a result of these challenges, many students drop out from their PhD programs.

Best PhD Programs

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What Courses Are in an Online Machine Learning PhD Program?

The courses in an online machine learning PhD program include an introduction to machine learning and deep learning, artificial intelligence, statistical theories, data mining , system simulation, computer programming, and software development.

Main Areas of Study in a Machine Learning PhD Program

• Machine learning
• Deep learning
• Artificial intelligence
• Databases and data mining
• Statistical theory
• Software engineering
• Systems simulation

How Much Does Getting an Online Machine Learning PhD Cost?

On average, it costs $19,314 per year to get a PhD in Machine Learning, according to the National Center of Education Statistics (NCES). However, this figure is not fixed, as the total tuition for a PhD program varies from school to school.

Private institutions generally cost more than public institutions, but there are funding opportunities for PhD students. Some PhD programs may guarantee financial aid for all their students regardless of merit.

How to Pay for an Online PhD Program in Machine Learning

You can pay for an online PhD in Machine Learning by taking advantage of student loans, scholarships, grants, teaching and research assistantships, graduate assistantships, and fellowship assistantships. As a result, most PhD students spend less than the tuition fee displayed on a school’s website.

How to Get an Online PhD for Free

You cannot get an online PhD in Machine Learning for free. However, there are ways to reduce the cost, or get partial tuition discounts and stipends through graduate assistantships, fellowships, scholarships, or grants.

What Is the Most Affordable Online PhD in Machine Learning Degree Program?

The most affordable online PhD in Machine Learning based on cost per credit is at Aspen University in Phoenix, Arizona. It charges $375 per month, which, when multiplied by the 67 months it takes to complete the program, results in a total of $25,125 for the entire program. This is more affordable compared to a school like Clarkson University, which charges $1,533 per credit hour.

Most Affordable Online PhD Programs in Machine Learning: In Brief

Why you should get an online phd in machine learning.

You should get an online PhD in Machine Learning because having a PhD offers you a stronger advantage in terms of employability, salary, and in your career in general that would otherwise be unavailable with just a bachelor’s and master’s degree.

Top Reasons for Getting a PhD in Machine Learning

• Research opportunities. PhD students get the opportunity to be involved in rigorous and innovative research that may positively impact humanity, add to the world’s knowledge, and improve the lives of others.
• Expertise development. A PhD is the highest level of academic degree, and as a result, PhD holders have expert-level knowledge in whichever field they acquire a PhD in. However, it is advised to only get a PhD if you are very interested in the field and willing to explore your interest and expand your understanding through cutting-edge research.
• Access to better jobs. There are lots of bachelor’s and master’s degree graduates in the job market, and earning a PhD will help you stick out from the crowd. A PhD reveals career opportunities that may not be available to bachelor’s and master’s degree grads.
• Networking opportunities . During a PhD program, students are in contact with top lecturers and academic experts by attending guest lectures, conferences, seminars, and workshops. Students can network with colleagues and classmates, which helps put them in a good position after their academic journey.

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What Is the Difference Between an On-Campus Machine Learning PhD and an Online PhD in Machine Learning?

The difference between an on-campus machine learning PhD and an online PhD in Machine Learning is primarily the mode of learning. Online PhDs are as rigorous and effective as their on-campus counterparts.

However, there may be some slight differences between the two in terms of cost, schedule, quality, and funding. Some of the differences that may exist are discussed below.

Online PhD vs On-Campus PhD: Key Differences

• Affordability. An online PhD is more affordable compared to the traditional on-campus alternative. An on-campus PhD can cost as much as $30,000 per year, while an online PhD may be as low as $20,000 per year.
• Flexibility. Online PhD students have the liberty to conduct in-depth study and research at their own time as opposed to the schedule of an in-person PhD program. Moreover, most online PhD programs don’t have an enrollment date, and some online PhD work is asynchronous, meaning students can take classes from anywhere at their convenience.
• Quality. Traditionally acquired PhDs are thought to be superior to their online counterparts by some employers and academics, probably due to sentiment. However, the quality of an online PhD is dependent on the research subject, the school’s reputation, and accreditation.
• Availability of funding. Funding available for online PhD programs may be limited due to some geographical constraints. For example, online PhD students cannot take up teaching assistantship positions unless they are willing to be physically present.

How to Get a PhD in Machine Learning Online: A Step-by-Step Guide

To get a PhD in Machine Learning, you need to first apply online to a PhD program. If accepted, you must enroll in the required classes and complete the academic coursework, research, and a series of academic milestones, which include attaining candidacy, passing the qualification examinations, proposing, writing, and defending your dissertation.

To begin your journey to acquiring a PhD in Machine Learning, you first need to apply online to the school of your choice. You also need to fulfill the admission requirements, including possessing a master's or bachelor's degree–depending on the school–in a relevant field, a minimum grade point average, letters of recommendation, and GRE test scores . 

Many online PhD programs require students to take and pass a minimum number of credit hours in core and elective courses. A typical online PhD in Machine Learning program consists of about 70 to 90 credit hours that involve intensive research in a provided or chosen area of concentration. 

Obtaining a PhD in Machine Learning allows an individual to become a world-renowned expert in the field. After completing a rigorous course of study and passing a series of exams, the doctoral candidate would then undertake an original research project that contributes new knowledge to the field. Upon successful completion of the degree, the graduate would be able to pursue a career in academia or industry. 

Examinations are an essential part of any education. They test a student's understanding of the material and help them to learn and remember the information. If you want to earn a machine learning PhD, you must pass the examinations for various core and required courses. Then, you will need to complete and defend your dissertation.

A dissertation is a research paper that is submitted to and defended by a graduate student to earn a graduate degree. To graduate with a PhD in Machine Learning, you are required to write a dissertation on a topic related to machine learning. Your doctoral dissertation must demonstrate your knowledge and understanding of the field of machine learning, as well as your ability to conduct original research in the field.

Online PhD in Machine Learning Salary and Job Outlook

The job outlook for machine learning jobs is 22 percent between 2020 and 2030 , with the number of new jobs expected in this time frame being 7,200, according to the US Bureau of Labor Statistics. The average salary for computer and information research scientists, which is a category that machine learning professionals belong to, is $131,490 per year .

What Can You Do With an Online Doctorate in Machine Learning?

With an online doctorate in machine learning, you can qualify for specialization roles and lead machine learning positions, including senior machine learning engineer and computer research scientist.

Depending on your preferences, you may also opt for a research and academic career path to become a university professor. The list below is a list of the best jobs for PhD in Machine Learning graduates.

Best Jobs with a PhD in Machine Learning

• Senior Machine Learning Engineer
• Computer and Information Research Scientist
• Data Scientist
• Software Engineer
• Postsecondary Teacher

Potential Careers With a Machine Learning Degree

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What Is the Average Salary for an Online PhD Holder in Machine Learning? 

The average salary for a PhD in Machine Learning holder is $108,000 per year , according to PayScale’s salary for skills in machine learning. The average salary a PhD holder receives depends on the location and position you apply for.

Highest-Paying Machine Learning Jobs for PhD Grads

Best machine learning jobs for online phd holders.

The best machine learning jobs for online PhD holders are typically high-paying jobs that require advanced-level skills that coincide with the nature of the position they undertake. Below are some typical job titles that online machine learning PhD degree holders assume.

A senior machine learning engineer oversees a team of machine engineers charged with designing and developing effective machine learning and deep learning solutions implemented in machine learning systems.

• Salary with a Machine Learning PhD: $153,255
• Job Outlook: 22% job growth from 2020 to 2030
• Number of Jobs: 33,000
• Highest-Paying States: Oregon, Arizona, Texas

Computer and information research scientists research and develop new ways of solving complex computing problems and apply existing technology. They work to significantly increase the knowledge in the field of computer science, which will aid in the production of more efficient software and hardware technologies.

• Salary with a Machine Learning PhD: $131,490

A senior data scientist is responsible for developing data mining and machine learning techniques to solve complex business problems. They identify patterns and trends in large data sets, develop models to improve forecasting and decision making, and effectively communicate data-driven insights to non-technical stakeholders and lead a team of data analysts.

• Salary with a Machine Learning PhD: $127,455

A software engineer is a professional that develops and maintains software. They work on a variety of software, from operating systems to video games, and may be involved in the development of websites. They must also have an excellent understanding of computer programming languages and be able to solve complex problems.

• Salary with a Machine Learning PhD: $121,115
• Number of Jobs: 1,847,900
• Highest-Paying States: Washington, California, New York

Postsecondary teachers are in charge of lecturing students in colleges and universities. They are also responsible for instructing adults in several academic and non-academic subjects including career, work, and research.

• Salary with a Machine Learning PhD: $79,640
• Job Outlook: 12% job growth from 2020 to 2030
• Number of Jobs: 1,276,900
• Highest-Paying States: California, Oregon, District of Columbia

Is It Worth It to Do a PhD in Machine Learning Online?

Yes, it is worth it to do a PhD in Machine Learning online. Getting a PhD is not for everyone, as the process will require tremendous effort and discipline, but it can be rewarding. A PhD in Machine Learning online allows you to learn from some of the best minds in the field.

You can also specialize in an area of your choice, such as big data, natural language processing, or deep learning. Specializing in one area for your PhD in Machine Learning allows you to deep-dive into that subject and build doctorate-level expertise.

An online PhD in Machine Learning provides students with the same high-quality education as a traditional PhD but with more flexibility and affordability. You’ll have access to top-notch instructors, state-of-the-art technology, and a thriving online community of experts.

Additional Reading About Machine Learning

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Online PhD in Machine Learning FAQ

Yes, you should get an online PhD in Machine Learning if it is critical for your career prospects. An online PhD in Machine Learning allows you to learn at your own pace and keep your day job while you pursue your degree. In the end, it sets you up for the highest-earning jobs in the machine learning industry , with better pay and a larger professional network.

The type of research you will carry out as a machine learning student includes research in deep learning, neural networks , machine learning algorithms, supervised and unsupervised machine learning, predictive learning, and computer vision. Students will make use of quantitative and experimental methods of research as well as the use of optimal feature selection.

You can choose a concentration for an online machine learning PhD by factoring in your interests, strengths, and career goals. You may also consider recent trends, the average salary of machine learning professionals , or the career options the machine learning industry has to offer when choosing a machine learning concentration.

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Tech at Work: What GenAI Means for Companies Right Now

Wharton professor Ethan Mollick says your team should bring generative AI into everything they do – right now.

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If you’re a senior leader, managing technology has never been more challenging—especially as organizations struggle to deploy generative artificial intelligence. Since ChatGPT burst into the mainstream a year and a half ago, everyone has been scrambling to make sense of how to use these tools, what they can and can’t do, and what they mean for our work and our teams.

Tech at Work is a four-part special series from HBR IdeaCast . Join senior tech editors Juan Martinez and Tom Stackpole for research, stories, and advice to make technology work for you and your team. New episodes publish in the IdeaCast feed every other Thursday starting May 2, after the regular Tuesday episode.

In this episode, Ethan Mollick , a management professor at The Wharton School of the University of Pennsylvania and author of the new book Co-Intelligence: Living and Working with AI , discusses what he’s learned through direct experimentation with these tools, where he sees the most potential, and why organizations are struggling to create value with them.

And please let us know what you think of the series and which technology topics you want us to cover at [email protected] .

Further reading:

• ChatGPT Is a Tipping Point for AI (Ethan Mollick)
• Why You (and Your Company) Need to Experiment with ChatGPT Now (HBR IdeaCast)
• The Social Cost of Algorithmic Management (Armin Granulo, Sara Caprioli, Christoph Fuchs, and Stefano Puntoni)
• Deployment of algorithms in management tasks reduces prosocial motivation (Armin Granulo, Sara Caprioli, Christoph Fuchs, and Stefano Puntoni)
• When AI Teammates Come On Board, Performance Drops (Juan Martinez)
• Super Mario Meets AI: Experimental Effects of Automation and Skills on Team Performance and Coordination (Fabrizio Dell’Acqua, Bruce Kogut, and Patryk Perkowski)

TOM STACKPOLE: For the last year and a half, we’ve been hearing that generative AI is going to change everything. In that time, companies have invested huge amounts of time, money, and resources, and most of them are still waiting for the payoff, even as it seems like everyone else is on the cusp of cracking how to use this technology. If you’re a leader, you’re probably asking, “Is this real or is this just hype?” and honestly, here at HBR, we’re asking ourselves the same thing.

Welcome to Tech at Work , a four-part special series of the HBR IdeaCast . I’m Tom Stackpole.

JUAN MARTINEZ: And I’m Juan Martinez. Every other Thursday, we’ll bring you research, stories, and advice about the technology that’s changing work and how to manage it.

TOM STACKPOLE: We’re both senior editors covering technology here at the Harvard Business Review, and in the last year, we’ve seen more pitches than we can count on generative AI. Juan, how would you say the questions we’re seeing have evolved over the last year, and where are we now?

JUAN MARTINEZ: I’d say that in the beginning, everyone was asking what’s possible. Now they want to refine their use. They want to figure out exactly how to use it perfectly for their business case with their employees, the way that their security structure is set up. It’s about getting everyone on board, making sure you can have it work within the organization, and then optimizing it. And so that’s where I think we are now, that optimization stage.

TOM STACKPOLE: I think I’m feeling a little more skeptical than you are about this, but I’m curious, what are the big questions that you have right now, and what would convince you that this is the real thing?

JUAN MARTINEZ: GenAI is not going anywhere, so the question now is, how do I use it, how does my team use it, and how does my company use it. There are a lot of questions that they’re going to have to answer, and just get in there and start playing around and learn how to converse with AI. It’s really important that you do that whether we’re using GenAI 10 years from now or whether we’re just using regular AI to answer our customer service questions.

TOM STACKPOLE: So I think you make a lot of good points. For me, there are still a few things that give me pause, especially when thinking about adoption at scale. This could have huge ecological costs, it could really change our media environment, but thinking about the questions that businesses need to be able to answer specifically, one, what do we want to be able to do with these tools, two, what impact will trying to do that have on our employees and our customers, or to say that another way, what are the risks, and three, are we confident enough in the promise of this tech to really invest in it and see what it can do. And maybe I’m just naturally pessimistic, but I see the high compute costs, I see risk around copyrighted materials, I see trust issues with employees, I see regulation coming down the pike, and I still don’t see proven use cases that are going to make it all worth it, but tell me why I’m wrong.

JUAN MARTINEZ: It’s the kind of thing like 40 years ago, if I had told you that there was a way for people to send letters to each other that would arrive instantly, you would’ve said, “Wait, hang on a second now. People could intercept those messages, and it’ll turn up compute power because people will be sending so many messages all the time,” and these are really good questions that you would’ve asked about email, but we’re using email, and we started using it, and we had to figure out how to use it because it wasn’t going anywhere. It was too convenient and it was too impactful for businesses to just pretend that it’s not there because they’re concerned about these questions.

TOM STACKPOLE: Well, I think our guest today will probably agree with you. Today, we’re talking to Ethan Mollick, a professor of innovation at The Wharton School at the University of Pennsylvania, who has become one of the leading experimenters with these new tools.

Way back in 2022, right after OpenAI launched ChatGPT, I called Ethan to ask him to write an article for us because he seemed to have an immediate intuitive understanding of how to use it. Now, a year and a half later, he’s just published a new book, Co-Intelligence: Living and Working with AI, about what he’s learned about using generative AI, what it can and can’t do, and the risk companies face in trying to integrate it into their work. We start out talking about what Ethan has learned through direct experimentation with these tools.

ETHAN MOLLICK: So the crazy thing about the state of AI right now is that nobody knows anything. I talk to all the major AI labs on a regular basis, we have conversations, and I think people think there’s an instruction manual that’s hidden somewhere. There is not. Nobody knows anything. There’s no information out there. So the best thing I could do is, this is the principle of my book, is that you should use AI for everything you legally and ethically can because that’s the way you get the experience with how these systems operate. So for me, I will use it for research and for writing. I use AI as an experiment for most things that I do to see what it works for and what it doesn’t, and the results are often quite surprising. So I think a lot of people are waiting for instructions that are not forthcoming, and you have to sort of take charge and do this yourself to some degree.

TOM STACKPOLE: For people who are kind of anxious about this, do you have a pitch for why people should start messing around with this or even just trying to figure out what it’s actually like to use this?

ETHAN MOLLICK: I have a few pitches. I mean, the first pitch is I think it’s important. I think a lot of people think this might be going away or, “AI is here, and now I got time to adjust.” This is a rapidly advancing technology, and I don’t think there’s any indication in any circumstance that it’s going to disappear. I also don’t think we’re going to see the advances plateau that quickly. Maybe a year from now, maybe two years, but it already operates at a very high level. I think we need to get used to a world that has AI in it, and trying to put that off doesn’t help you, and in fact, knowing how it works will help you adjust as the systems get better.

The second reason is that it is helpful. I mean, it’s funny because when I actually talk to large groups of employees and executives, the executives almost always are not using AI, but lots of employees are already automating their job. Right now, the huge advantage comes to you as a user. If you can figure out a way to make your job better, then you use AI to do that. And a lot of people are secretly using AI at work. I think the best survey we have is over 60% of people use AI secretly at least some of the time. And so there’s advantages to you. We can talk about what that means for organizations and what that means for leaders in just a bit, but there is value in doing that.

And then I would say the third reason is once you get over the freakiness, it’s super interesting and fun to explore. Like this system does a lot of things that are really neat, and you can be the first person to figure out what those things are.

JUAN MARTINEZ: In my mind, you’re like the prompt master. I go to your LinkedIn page, I see prompts after prompt, like you’re giving good feedback. What research would you cite to talk about the best prompts and the best way to use the answers? And do you have your own best practices for how to use prompts and how to take answers?

ETHAN MOLLICK: So there are two kind of core ways to interact with the AI that we call a conversationalist or interactionist and structured. And so in a conversational prompt, you’re literally just chatting back and forth with the AI, and everything matters in this case. There’s papers showing that punctuation matters, that if you ask the questions in a dumber way, you get less accurate answers. If you approach it as a debate, the AI will argue with you. If you approach it as, “I am the teacher, you’re the student,” and you sort of imply that’s what you want, the AI will be much more pliable. If you approach it as, “You are a dumb machine that just does work,” it’ll act like a machine that just does work. We don’t actually fully understand how to make that happen best, so I just don’t worry about it. Like I said, I give it a context, “You are X, I am Y. Let’s work together on this,” and that gets you a large part of the way there.

Part of the problem is that they’re putting too much magic in prompting. There is not a single one of the AI lab people I talked to that think prompts are going to be that important two years from now or that like prompt crafting or prompt engineering is a skill. It will be if you’re just trying to build large-scale enterprise deployments, but for most people’s work, the AI already can tell you what to do and it’ll only get better at having you prompt things. When I teach my students how to prompt, I typically make them prompt four or five times before producing something, and interestingly enough, by the time you’ve prompted four or five times, not only is it hard to recognize it’s AI writing, but also the AI detectors don’t work anymore. So it feels much more like a blend of human and AI work. So this is not a type a query in and get a result back. It’s a conversation with an intern, with an employee that you are trying to get to do good work.

TOM STACKPOLE: In the book, you talk a lot about how you used AI to write this. Can you tell us a little bit more about what that process was like, what you learned worked well, what didn’t work well, how that whole process changed how you were thinking about using these tools?

ETHAN MOLLICK: So one of my principles in the book is to be the human in a loop, to figure out what you’re good at and do well. For right now, where AI is on the ability curve, you are probably better on it at whatever core task you like to do most, and AI is probably not as good as you at that.

So I like to style myself a pretty good writer, and AI is not as good a writer as me, but the AI made writing the book much better because it did a lot of ancillary tasks. So that could be anything from the stuff that typically stops you from writing a book, which is, “Give me 20 versions to end this sentence because I’m stuck.” I would ask the AI to just give me variants. You know, “Summarize these 200 research papers,” and I would read them myself, but then I had the summaries available to work from. I’d actually send the summaries to some of my fellow academics, and they quite liked them. They thought they were well done. AI does a pretty good job simulating customers, so I had AI readers read chapters and give me feedback on it.

So it wasn’t about the writing task itself, it was about removing the friction from all the other stuff that would’ve stopped me from doing my writing, and I think that’s part of what you want to think about when you’re using AI, is not so much how do I replace the core thing I love to do and then I think I’m better at. Maybe AI could do that, maybe not, but it’s about how do I make it so that’s what I get to focus on.

TOM STACKPOLE: So one of the things that you write about that I think is really useful is you have a great way of thinking about the kinds of work tasks that we should continue to do versus the kinds of tasks that GenAI might be able to take on. Can you kind break these down into categories for us?

ETHAN MOLLICK: Sure. So there are sort of four categories and a subcategory, not to make it hard. But I talk about Just Me tasks, which are tasks that only you as a human can do, and that might be because you want to do them, or it might be because only a human should do them because a human should remain in the loop of that particular task or idea.

JUAN MARTINEZ: You’re a professor. What are some of your Just Me tasks on your day-to-day?

ETHAN MOLLICK: So an interesting Just Me task in my case is letters of recommendation. It’s about purposefully setting my time on fire as a signal flare to people that I care about someone. So I’m supposed to spend 45 minutes or an hour doing that, and I’m supposed to struggle with what I write. If I just give the AI the resume of the person, the job they’re applying for, and say, “I’m Ethan Mollick,” and do a thumbs up emoji, “Write a letter of recommendation,” I will get a better letter of recommendation than the one I would write in 45 minutes or an hour.

So there’s an open question to me about whether or not we do that or not. You know, I still do grading by hand even though I know the AI will do a better job because I feel like that’s an obligation as a professor. When I write reviews of academic papers, I do them by hand, but in the principle of I use AI for everything, I turn them in and then I have the AI then do a review and see where the AI and I differed. So I’m trying to think about these things, but those are kind of moral lines that may get crossed soon.

So those are Just Me tasks. Then the other two categories are delegated tasks, where we do some of the work with the AI. And then zooming out again, we finally have automated tasks. You say, “Go handle this,” and it goes and solves your problems for you. And that’s the explicit goal of OpenAI this year, is to release fully-working autonomous agents.

TOM STACKPOLE: Yeah, I mean, one of the things that’s been interesting with critics of some of these LLMs is people were saying, “There are limits to this architecture. There is going to be a plateau, and it’s going to be coming sooner than people think.” I mean, what do you kind of think of that argument?

ETHAN MOLLICK: So I think that nobody knows the answer, and I see splits even when I talk to people at OpenAI between we’re on an infinite curve here to AGI versus this is going to even off, and I don’t think anyone knows, and people don’t know until the training is done and the model is shipped essentially. So I would suspect that this summer of 2024, we will see GPT-5 class models that will represent a significant improvement over GPT-4. After that, that’s the next question. Is there a GPT-6 that is big a leap over GPT-5? Are we seeing the top of the curve? I don’t know the answer.

JUAN MARTINEZ: I know how I feel personally about working with AI or having AI work for me, but there’s a lot of studies now happening that are talking about AI as a boss, AI as a supervisor. Can you talk a little bit about what that means and how people are responding to being controlled or supervised by AI?

ETHAN MOLLICK: So we don’t actually have a lot of evidence on the current version of AI. Part of the problem that happens is our conversations about AI are confusing because prior to ChatGPT, when you would have a podcast or an HBR article about AI, it was about AI as algorithmic approaches that were usually math-based about forecasting. And so when we talk about AI supervision, almost all the studies are about these earlier models that were sort of cold algorithmic control, so what’s it mean to be an Uber driver and have the algorithm telling you what to do, and what happens when doctors or managers are getting advice from AI and how do they feel about that, and I think that we’re not seeing the same sort of effect because large language models feel like it’s already the person. It feels warmer, it feels more humane, and we don’t quite know what the effects are going to be. That being said, I think the dangers are still there of algorithmic control. It’s just dressed up in a nice way.

TOM STACKPOLE: In this context, you’ve talked about how important it is to still have expertise. My first magazine job was being a fact-checker, and it was brutally slow and surprisingly hard because there’s facts kind of baked into all kinds of things and it’s not always immediately obvious what kind of assumptions are being made. So how do we make sure that we can still be experts and do this kind of fact-checking work?

ETHAN MOLLICK: Expertise is kind of great because it gives you heuristics and rules. You can glance at something and say, “Is this good? Is this bad?” Right? And you build expertise through deliberate practice, from trying something over and over again and getting feedback on it.

To me, the biggest risk, actually, is the destruction of deliberate practice inside organizations because the way we do this is we actually have a medieval apprenticeship system inside white-collar work, right? When my students graduate from Penn, I think they’re awesome, but they’re not specialized at working at HBR, or Goldman Sachs, or McKinsey, or whatever, name your company of choice. They go there and they spend a couple of years learning the ropes. That’s how we sort of teach people, is like we get an intern who is very, very smart but inexperienced, and in return for doing some of our work, they learn, right? And they get paid relatively little for their job, and then if they’re good enough, they advance in the organization. That’s the basics of how organizations work.

Intern work is the most delegatable work to AI. It’s so easy to hand off, like, “Write this deal memo, do this research project, give me the initial briefing for this interview, create a transcript and highlight the key points.” And what I think the real danger is that we’re going to destroy expertise-building inside organizations because people are just going to have AI do that work for them.

JUAN MARTINEZ: Well, you’ve actually studied this in a real-world setting. Can you talk a little bit about the study with BCG. They were given ChatGPT-4 to perform some of their work tasks. How did it work, what did they do, and what did you learn from it?

ETHAN MOLLICK: So this is work with a whole set of co-authors at Harvard, MIT, and Warwick. We went to Boston Consulting Group, so elite consulting company. They gave us 8% of their global workforce, which was amazing, and we did a couple different experiments. One of the main ones was we developed 18 realistic business tasks, and these ranged from analysis, to persuasion, to creative tasks, and we asked people to do these tasks. We measured them before, doing the tasks without AI, and then in the second set of tasks, half the people randomized in using AI, half not.

What we found was pretty extraordinary, which is a 40% improvement in the quality of answers, and about a 26% improvement in speed, and about a 12.5% improvement in the amount of work done, and that was with GPT-4 out of the box without any of the special training lots of companies are spending their time and money trying to build. We found the impact largest on the bottom performers, not on the top performers, though we’re still trying to figure out whether or not that was a result of early use, where people didn’t know very well, or whether that’s a universal thing, though that’s a result people have found before. And so very, very powerful results right out of the box for a very elite set of tasks, which was fascinating.

JUAN MARTINEZ: So if you’re an enterprise, how do you sort of take your weakest “employees” and then give them ChatGPT-4 and help them become better?

ETHAN MOLLICK: I think if you’re an enterprise owner, there’s a lot to think about, or as a manager, because the incentive right now is for your employees to secretly use these systems, and they’re doing that all the time. You could think about the reasons. There’s a lot of them, right? In one case, like, “What if my rules aren’t clear and I get fired for using it?” Second is, “You guys think I’m a wizard right now because I’m suddenly producing all this amazing work and you don’t know how I’m doing it. If you know it’s AI, you might value my work less.” Or maybe you start to see, “Hey, I just showed that I’m replaceable by AI. I don’t want to show you that.”

So there’s a lot of reasons people don’t want to share. So it starts with a culture problem and an incentive problem, right? How do we incentivize people to do this? How do we build a culture that people want to share what they do? So I find in startups, in nonprofits, in cooperative enterprises, they’re really hitting it off with AI because people share, “Hey, I figured out a way to do something cool.” In large-scale bureaucratic organizations or highly-competitive organizations, everyone’s hiding their AI use all over the place for all those reasons.

JUAN MARTINEZ: Can you give us examples, if you have any, of these secret cyborgs that really messed stuff up because they just copied and pasted or because they didn’t do the work that you suggest people do in order to make the most of GenAI?

ETHAN MOLLICK: The weird thing about it is like the secret type of work people are doing at work often are doing it for tasks they know well and are experimenting till it’s good because they know what a good task looks like. So I haven’t seen huge incidents inside work environments, right? There’s famous cases of lawyers using this, thinking it works kind of like Google, and finding sites for themselves that aren’t real, it hallucinates sites all the time, and not checking and getting in trouble with judges. That’s become increasingly a sort of phenomenon in the legal field. Again, something that we’ll see growing, but it’s from kind of inappropriate use. Like for example, one use I see people putting this to that’s completely inappropriate and it was one of the most common things people tell me they use the system for is performance reviews. Of all the use cases, right? Performance reviews suck to do, but they are meaningful by the process of doing them, right? And when there’s only a couple AI users, maybe it doesn’t feel so bad, but after everybody starts using them, we have to rethink about how HR works in that case.

TOM STACKPOLE: I think the story is about the hazards of how this could be applied by companies are really interesting, and I want to look at a different study, this one by Harvard Business School researcher Fabrizio Dell’Acqua, and he studied recruiters using AI. Some were given a good AI, some were given a mediocre one. So how does this play out, and what does this example tell us about how companies should be careful about how they’re starting to use these tools?

ETHAN MOLLICK: And Fabrizio’s the lead author on the BCG study as well, and he studied in this a phenomenon that we found a bit large in the BCG study, which is that when people use an AI system that’s good enough, they actually stop paying attention. He calls it falling asleep at the wheel, and it’s the most common when the systems are best. So if you have a bad AI system, you’re checking all the work. If it seems like it’s really smart or good, you stop checking it. There’s an additional factor, which is that the AI’s errors actually become very subtle, so it’s very hard to even check the facts.

So between those two factors, fact-checking becomes as hard and we fall asleep at the wheel, it means that you stop using your brain as much when you’re using AI work, and it’s really hard to figure out a good way around that. But there’s no sort of Boeing-style disaster at this stage of somebody turning something in, right? It is much more a bunch of small disasters of people not paying attention. So I think that’s the real danger to me, is less that we’re going to see a plane fall out of the sky and more that we see a steady creeping set of indifferent work appearing.

TOM STACKPOLE: Coming up after the break, we’re going to talk about what organizations that are successfully using generative AI are doing differently and why these tools can’t be managed like other enterprise technology. Be right back.

So one of the things that you articulate I think really well in this book is that there’s this tension between how easy it is for individuals to be innovative with these tools and it’s really, really hard for institutions to do the same thing for a variety of reasons. And so what question should companies be asking as they start to figure out what to do about generative AI, how they should be thinking about it, what it means within the organization?

ETHAN MOLLICK: The truth about innovation is it’s very expensive to do. R&D is expensive, and the reason why R&D is expensive is because the way we learn is trial and error. It’s why drug trials are very expensive, it’s why figuring out whether a software product is good or bad is very expensive. But R&D is very easy for people at the tasks they do at their own job, right? Where if we try recording this podcast slightly differently every time, if every time you send an email out, you’re doing it slightly differently, like that’s pretty costless and you get fairly fast feedback because you’re experimenting in a domain you know well and you could do it all the time, and that’s how we learn.

The problem with organizations is that they’re viewing this as an IT product that needs to be centrally controlled and implemented, and there’s a lot of problems with that kind of central control approach with AI. You’re waiting for centralized instruction to tell you how to use it, and it’s unclear how a senior management team would be able to tell a line worker how to improve their sales technique using AI, or if they’d listen anyway.

JUAN MARTINEZ: Have you come across any examples of companies that have incentivized this use well and have actually brought use cases to their employees and said, “Hey, do this. It’ll help you”?

ETHAN MOLLICK: I mean, one of the more both effective, I think, and also more extreme effects was IgniteTech, which is a software holding company. The CEO basically got into the idea of AI very early and gave everybody GPT-4 access last summer and said, “Everyone needs to use it,” and he has told me that he then fired everybody who didn’t put a couple hours in by the end of the month, but he also offered cash prizes to anyone who gave the best prompts. Another organization I know, when they do hiring, they require you to try and automate the person’s job with AI before you put the job request out, and then you put in a different job request that’s altered for what you think the job’s going to look like in the future. So I think modeling behavior, incentivizing with rewards, and thinking about the future are the three things you want to be able to do to incentivize organizations properly.

TOM STACKPOLE: It sounds like you’re also saying that companies need to really change how they’re thinking about where innovation comes from and who’s responsible for it and who’s being rewarded for it, right? I mean, there’s also kind of just sort of a structural or kind of even just how they’re thinking about this that needs to change.

ETHAN MOLLICK: I absolutely think that we’re not ready for this world that’s happening. We’ve built corporations around the idea that the only control system we have are other humans and that the only way to get advice is to escalate things up a chain, and that’s not true anymore. So organizations need to change in lots of ways. The locus of innovation has always been on the edge, but now it matters more than ever because your only advantage as a large company with AI is that you have more people using it and you can adapt faster. Otherwise, everybody else has the same AI tool you do, and most companies I talk to have worse AIs than every kid has access to in most of the world because they’re so scared about privacy and other concerns, sometimes rightly, sometimes wrongly, that they don’t allow the most advanced version.

TOM STACKPOLE: You know, we’ve had this kind of natural experiment where Bloomberg invested in their own GPT, and now we’re looking at how that compares to frontier models basically for doing the same task. You have one that’s just out there and one that’s been trained with all this really valuable proprietary data. What is that sort of telling us? What have we seen in that kind of experiment?

ETHAN MOLLICK: So just for people who aren’t that familiar, frontier models are the most advanced models, and right now, there’s a very strong scaling law in AI, which is the bigger your model is, which also means the more expensive it is to train, they’re just smarter, and the result is that the most advanced frontier models are often much better than specialized models built for specialized tasks.

And we don’t have all the answers yet, but Bloomberg decided to build a finance GPT, and they spent over $10 million on it from what I can tell, and they trained it on finance data, and it was supposed to do things like sentiment analysis for stocks and so on. And it did that, and it was pretty solid, but then in the fall, the team retested that compared to GPT-4, which is the advanced model available to everybody all over the world, and GPT-4 beat it in almost every category. We’re seeing the same sort of effects for specialized medical models, being beaten by GPT-4, which is not built for medicine, or especially law models in the same way. What this tells you, as a company, you’re going to be kind of thinking about the use cases. If smarts is valuable, you’ll need to use a frontier model, but the future is probably not training your own AIs.

TOM STACKPOLE: One of the things that’s kind of surprising is I think a lot of companies are looking at generative AI and they’re saying, “This is great. We can cut headcount. We can automate tasks, so now we can have 10 people doing the work that 80 people used to do.” But I’m curious what you think of that instinct of, “This is a labor-saving tool. Let’s cut headcount. Let’s keep doing the same stuff with fewer people and just count the bonus profits.”

ETHAN MOLLICK: To me, I mean, that is a short-sighted failure of imagination in many different ways. Seriously, if you really think that this is another general-purpose technology like electricity, steam, or computer but happening in a very compressed time period, then the worst thing you could do is say, “Let’s make sure to keep productivity the exact same by firing people who might be the people who could leverage this into the next generation of things.” And I see that happening. People will reduce headcount to keep performance the exact same at the exact same moment that everybody else is getting a performance boost. What I fear is that companies have gotten into the view that cost-cutting is the highest-value thing they can do. Expansion was hard to do. You do expansion through acquisition. Now you would do expansion through your workers, through everyone becoming more productive, and they need to change their mindsets or workers will detect that and they’re not going to come along as part of the voyage.

I mean, I have these rules for organizations that I’ve been thinking about which is four questions I would ask any company, which is, one, what did you do that was valuable that’s no longer valuable? If providing white-glove customer service to everybody was your big differentiator, that’s about to go away.

The second thing though is, there’s something impossible that you could do that you couldn’t do before. What is that? So if you’re a consulting company, it used to be 10 hours or 20 hours of someone’s work to give you a basic outline of a piece of work. Now you can produce a bunch of stuff with a consultant’s mindset in five minutes. What does that let you do? Do I offer individual advice to everybody? Does everybody get their personal consultant? It’s very exciting.

The third thing is, what can you move upmarket that you couldn’t do before? Like so now we can provide white-glove service to everyone. Your customer service agents know everything about a person and have a great interaction with them. Your salespeople can give personalized sales pitches.

And then the fourth thing is, what can you move down market or democratize? I think again about BCG. I think those results showed that it makes consulting much easier to do. They work for high-end Fortune 5000 companies. What can they do now for small and medium businesses that they couldn’t do before? How do they move down market?

So I think people who are thinking about this strategic shift will be much better off than people who don’t.

TOM STACKPOLE: Two final questions, I think. First, what is your advice for a manager or a team leader about how to get started with generative AI?

ETHAN MOLLICK: The advice I would give a manager is to start using it and using it publicly, model behavior, and the idea is that you’re going to show where you fail or succeed, you’re going to model curiosity, that you’re trying to figure out from other people how to use it, that you would also be publicly kind of sharing that you don’t know what you’re doing and working on it. I’d say, “Hey, let’s try doing AI in this meeting, and let’s have it recorded and give us advice, and let’s give it feedback on that advice.” I think making it casual and making it interesting, I think, is going to be really important.

TOM STACKPOLE: So what about for senior leadership? What should they be sort of doing with this?

ETHAN MOLLICK: Absolutely. I think the same things apply, but I also think that they have to realize that this is the real one, right? And if it is, this is enough of an emergency that you should be using it so you know what it does, and your employees should be using it, and you should be thinking about this, and you need a multi-pronged approach. How do we reorient organization? We don’t have answers yet, like we don’t know what an organization chart looks like with AI included, we don’t know what processes need to be changed. This is where leaders and strategy actually matters, and I would love to see more leaders stepping up and saying, “I have a vision for how to build a better organization in this time period.” You want to be the visionary leader of the next century that everyone looks up to and that there’s biographies about you. You figure out how to use AI in a positive way to build the next great enterprise. That’s how you get famous.

TOM STACKPOLE: Juan, do you want to get famous?

JUAN MARTINEZ: I’m already famous. I’m on a podcast with Ethan Mollick for Harvard Business Review. My mother never thought I would get this famous.

ETHAN MOLLICK: Well, nobody paid attention to me for like the last decade, so it’s been a very funny rise of like, “I’ve been talking about stuff like this for a while, but okay.”

JUAN MARTINEZ: Ethan, I learned so much from this, and your book was fantastic.

ETHAN MOLLICK: Thank you guys so much. This was really interesting.

TOM STACKPOLE: Thanks for coming on.

That was Ethan Mollick. He’s a professor of management at The Wharton School at the University of Pennsylvania. His new book is Co-Intelligence: Living and Working with AI.

So Juan, we touched on this super briefly with Ethan, but before we wrap up, let’s talk for a minute about how people feel about working with generative AI.

JUAN MARTINEZ: Yeah, this is a really, really important topic, and researchers are exploring it because it’s important to understand the challenges that we’ll face as AI is integrated into human teams. We’ve published a few articles on this already at HBR. One that I really love just came out in our May-June magazine issue. Full disclosure, I edited it, so you know it’s awesome.

TOM STACKPOLE: Okay, tell us about it.

JUAN MARTINEZ: First of all, Tom, have you ever played Super Mario Party?

TOM STACKPOLE: No. The last Super Mario game I played was probably Super Mario 3 on original NES.

JUAN MARTINEZ: All right. So a team of researchers use Super Mario Party to explore how integrating AI into a team affects humans. In the experiment, people were asked to play Super Mario Party together. They were paired in teams of two, and they had to work together to gather fruits and veggies from around the kitchen. They also had to coordinate with other teams to make sure their onscreen characters didn’t bump into one another, and they all had to do it really, really fast. So if you’re watching the game, you’d see these Mario characters rushing around a kitchen, grabbing tomatoes and lettuce, and maybe Mario is bumping into Gumba, and Toad has a huge stack of teetering plates. You can picture it, right? Very chaotic, very fun.

TOM STACKPOLE: And how does AI come into this?

JUAN MARTINEZ: The researchers asked these teams to play together for six rounds, the two humans. Then they added an AI team member and asked the team to play another six rounds with the AI. And even though the AI is really, really good at Super Mario Party, they found that team performance declined in all kinds of ways when AI was added. They looked at coordination, like how often Mario actually bumped into a Gumba, they looked at how many ingredients the teams gathered each round, and they asked all the people on the teams how motivated they felt when playing with the AI teammate, and all of that was worse with the AI. Here’s Bruce Kogut, one of the authors of this paper. He’s a strategy professor at Columbia Business School.

BRUCE KOGUT: So we had initially all humans playing this thing, but then suddenly we put in this change. You know, like we would take out their best friend Luigi and replace it by an AI algorithm, and they just did not like it. You had the humans increasingly getting more and more depressed over time playing with this AI-driven Luigi or Mario.

TOM STACKPOLE: So Juan, what are the takeaways for managers who may be trying to figure out how to integrate generative AI into their teams?

JUAN MARTINEZ: Eighty-four percent of the people in the experiment said they preferred to play with their human teammates over the AI teammate. That led researchers to conclude that when you add AI to the mix, team sociability can fall. That means human team members feel less motivated, less trusting, and they make less effort. So if you’re going to add AI to your team, keep that very real downside in mind.

TOM STACKPOLE: Okay. So what’s up next, Juan?

JUAN MARTINEZ: All right. So the second article was published on hbr.org in February 2024, and the focus is on how working for an algorithm, which is already a reality if you’re an Uber driver, changes workplace dynamics.

TOM STACKPOLE: Okay, we’re talking about algorithmic management, right?

JUAN MARTINEZ: Exactly, that’s it. They started by surveying workers in the transportation, distribution, and logistics sectors, and they found that workers who were managed algorithmically were less inclined to help or support colleagues, and this was true even when they controlled for factors like the size of the organization, employee turnover, type of job, income, gender, et cetera, et cetera.

Next, the researchers did a field experiment. They paid 1,000 gig workers from an online labor platform to create slogans for a van rental company’s social media marketing campaigns. The workers were randomly divided into two groups. One group was guided and evaluated by an algorithm, and the other group by a human. After the workers had completed the task, the researchers asked them to offer advice to others on how to create effective marketing slogans, and they found that the workers managed by the algorithm offered roughly 20% less advice to their peers than the workers managed by the person.

TOM STACKPOLE: Okay, but what about the marketing slogans? Was there a difference between the quality of their work?

JUAN MARTINEZ: No, that’s the thing. The quality of the actual slogans that the two groups came up with didn’t differ significantly, which suggests that algorithmic management doesn’t necessarily affect workers’ task-based performance, but it can decrease their pro-social motivation, and this was especially true when algorithms were monitoring and evaluating employee performance. Here’s Stefano Puntoni, one of the authors of this paper. He’s a marketing professor at The Wharton School and co-director of AI at Wharton. This is his advice for companies using algorithmic management.

STEFANO PUNTONI: So the moment that you are, as a worker, you feel you’ve been appraised by an algorithm, your performance, your worth, basically, as an employee is being measured by a machine, we find that then people tend to objectify coworkers and be less helpful to them. Companies really ought to think about the byproducts for organizational culture, for individual feelings and cognition when you deploy algorithm. It’s not just about can the machine do it. The question is, should you let the machine do it?

TOM STACKPOLE: Yeah. So it sounds like what he’s saying is that there’s really a cultural element that we need to be thinking about here because this can really change how people feel about each other, even if they’re not necessarily working with a machine coworker but if the terms of the system are being dictated by a machine.

JUAN MARTINEZ: Yeah, I mean, this is the early days, right? Nobody knows exactly how to use these tools to get the maximum benefit out of them, and every role within every organization is going to be a little bit different, so you have to figure out what the right option is for your organization, but then you have to go into use cases and start to figure out how it works for each individual role, and we’re definitely not there yet.

TOM STACKPOLE: Okay. Thanks, Juan.

Next time on Tech at Work , how will the end of third-party cookies change the internet? It’s a big shift that’s coming up fast, and it has huge implications for digital advertising and publishing and how all kinds of incentives on the internet really work.

JUAN MARTINEZ: We’ll talk with the researcher who’s studying how ad effectiveness will be affected, and we’ll speak with an agency executive who’s guiding her clients through this transition That’s in two weeks, right here in the HBR IdeaCast feed.

TOM STACKPOLE: Did you know HBR has more podcasts to help you manage your team, your organization, and your career, including nearly 1,000 episodes of IdeaCast alone? Find them at hbr.org/podcasts or search HBR in Apple Podcasts, Spotify, or wherever you listen. And if you want to help our show, go to your podcast app and rate the show five stars. It helps more than you may know, and we read every comment.

JUAN MARTINEZ: Thanks to our team, senior producer Anne Saini, senior editor Curt Nickisch, audio product manager Ian Fox, and senior production specialist Rob Eckhardt. Special thanks to our friends on HBR’s video and social teams, Nicole Smith, Ramsey Khabbaz, Kelsey Hansen, Scott LaPierre, and Elainy Mata. And much gratitude to our fearless leaders, Maureen Hoch and Adi Ignatius.

Thanks for listening to Tech at Work , a special series of the HBR IdeaCast . I’m Juan Martinez.

TOM STACKPOLE: And I’m Tom Stackpole. We’ll be back in two weeks.

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