Let’s take a break and think about the state of AI in 2022.
In this episode I summarize the long report from the Stanford Institute for Human-Centered Artificial Intelligence (HAI)
Enjoy!
If you want a new interactive experience, I am scheduling hands-on session on Twitch
Feel free to drop by when there is a live session, and interact with me. I’ll see you there!
References
https://spectrum.ieee.org/artificial-intelligence-index
https://www.twitch.tv/datascienceathome
Transcript
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This episode is supported by Tonic AI.
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With Tonic AI, this is the sound of turning ideas into software.
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This is the sound of engineering and passion.
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Work.
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Work more, work harder.
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Experiment.
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Build, break, and build again.
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Write code, improve it.
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Job done.
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Celebrate.
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Insurance.
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Finance, retail.
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Defense.
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Robotics.
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Energy.
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Amethyst welcome back to another episode of Datasensertone Podcast.
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I’m Francesco Podcasting from the Regular Office of Amethyst Technologies, based in Belgium.
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Today I’m speaking about the state of AI in 2022.
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This data comes from the usual report that is produced by the Stanford Institute for Human Center Artificial Intelligence.
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Hai they essentially put the AI index, which is a very large report of data and graphs and statistics about what’s going on in the field of AI and, of course, what we should be expecting in the coming years.
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Now, the links of the official report will be reported in the show notes of this episode, as always@datasensatom.com.
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But of course, you can also jump on the Discord Channel or Official Channel, where you can speak with the folks of the Data Science at Home Podcast and with myself.
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So in this episode, I would like to go through this summary of the report, and because I believe that it’s quite important, there are some news that we probably did not expect, some others that we definitely have seen in the last few years happening.
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So nothing super novel there, but it’s always good to make a state of the art of how things are and kind of a point of where we are at and where we are going.
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So the very first thing that I would like to discuss is about investment.
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So we have seen, if you look at the trend, from 2013 to 2021, there has been a massive global corporate investment in AI.
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And no big news here.
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We have seen this happening, we have heard many times, and we have read it many times on the news.
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But just to give you some numbers, when it comes to private investment, in fact, there has been an explosion, literally from about $9 billion in 2015 that number has grown to $15,000,000,000.02 335-4246 in 2020 and a massive 93 in 2021.
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So almost double that with a similar trend, very similar trend.
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We have seen also merger and acquisition in, of course, billions of dollars in the years.
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Very small number, 2015 and 2016.
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And then all of a sudden it starts growing to 22 billion of US dollars in 2017, 20 in 2019, and then again 21 in 2020 and the 72 in 2021.
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So again, two big trends here a lot of private investment in the last year and a lot of acquisitions and merged companies.
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So the story here, probably, if you want to give an interpretation to these numbers, is that it’s probably good to join a STARStar, as in making one.
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That’s at least what they conclude out of these numbers.
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But probably it’s not going to be a general case, but it’s a very good indication that we are in front of, I would say richer model in which these established corporations keep merging and keep acquiring technologies from small startups.
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We have seen that trend going already, up from 2017 and it doesn’t seem to stop anytime soon.
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At least that’s what the numbers are telling us.
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The second point is about the US versus China cross collaboration, crosscountry collaborations in AI publications.
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And there is a massive trend here that tells a very clear story that United States and China are in fact contributing to AI publications.
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There is a never growing increase in cross country collaborations when it comes to two countries.
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And the difference between only the second one, like UK and China, it’s just a different planet.
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We have a number of AI publications in thousands, it’s about more than 110 in 2020.
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And the second best, which is UK, china is only 3000 or a bit more.
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So different orders of magnitude there.
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We have seen that.
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Honestly, I didn’t expect that because we have heard a lot of geopolitical tension between US and China, and I personally did not expect to see those completely different stories in terms of collaboration and AI between these two countries.
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So that’s a good thing.
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In fact, I like that and I believe that good research comes also from a bit of competition, sometimes also together with collaboration.
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There’s always that kind of sweet spot between competing and collaborating that keeps things spicy and definitely improves research globally.
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When it comes to patents, however, the story is pretty different because China has, let’s say, record of applying to patents, but not the record of filing them.
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So these patents, they apply much more than United States, but these patents don’t get granted, essentially.
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So there are two different curves.
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There the application, which is China is basically dominating the rest of the world, but when it comes to granted patents, they are even below European Union and United Kingdom.
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And again, United States there tells a different story.
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They have several points up in percentage when it comes to granted patents, and also when it comes to applied patents, they have a much smaller curve than China, much lower curve than China, which tells us that in the United States, there is much more reasoning about applying to patents and much higher success rate.
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Which means that the papers, the research that gets produced by US.
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Researchers is more credible, in a way, at least this is what I believe these numbers are telling me.
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Patents cost money.
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You also have to defend from abuse, and that also costs money.
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So when you file for a patent, it means that you’re really sure that something tangible behind a patent is there.
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There is a research that can be, indeed can have a massive impact on a foreign application in a particular sector.
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Otherwise you wouldn’t be spending that much money for protecting that idea.
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And this means that in the United States, this feeling about registering a patent and protecting it, and of course, selecting credible research is much more evolved, is much more sophisticated than the rest of the world.
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Now, when it comes to technology, we have seen two big factors when it comes to AI and two fields of research, in particular, computer vision and NLP.
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In the years, we have seen these two fields going kind of hand in hand when it comes to outcome of research and accuracy, and also the power of these methods whenever they are applied in the real world, right? And when it comes to computer vision, we have seen a big improvement in, for example, recognizing images, but also doing facial recognition, object classification.
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We know that the current technology, which is a technology already three, four, probably five years old, already has kind of reached a plateau in terms of accuracy.
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We are good at recognizing objects.
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We know that there are several limitations when it comes to, for example, reasoning about the recognized objects.
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And there is a very classic example now about recognizing a scene in a picture and trying to understand what’s going on from another perspective, not just recognizing a ball or a person or a dog, right? We also want to understand what’s going on in the scene, like what’s happening and why, for example, the dog is not jumping or whatever is described by those images.
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Now, that’s where the problem essentially comes out, because artificial intelligence, and in particular, deep learning technology, we have seen it many times.
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We have said it many times as well on this show.
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It’s not sufficient for reasoning about images.
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And a very similar trend has happened in NLP.
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Natural language processing.
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We are good at, for example, recognizing or classifying the semantics of a paragraph, or even summarizing small text or relatively long documents into paragraphs.
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But reasoning about what’s written, it’s a different story.
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And we also are very familiar with the fact that natural language processing and language models in particular, are probably one of the most complex tasks that an AI would be asked to work on.
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Now, regardless of what they say with GPT-2 three, whatever other version out there, NLP hasn’t been mastered as a field.
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And this is exactly where all these limitations are essentially shown.
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Even in the report, reasoning is still a frontier of AI.
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We haven’t solved that problem, and I’m pretty sure, and I quote pretty, that with the current technology and with current algorithms, we cannot solve that problem.
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I believe, personally believe, that there is a, I’m not saying a plateau on deep learning techniques, but definitely we need different methods to think about and to solve the reasoning problem.
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When it comes to images, and of course, when it comes to natural language processing, the biggest change with respect to the past years has been shown in the ethics sector.
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So ethical AI and has essentially monopolized the research.
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In the last two to three years, we have seen literally an explosion in education.
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When it comes to number of papers.
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From 2018, we had something like 63 papers related to ethical AI and ethics, and these numbers literally doubled in 2019 to 139 papers then, 200 papers in 2020, and 227 in 2021.
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Now.
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This was kind of expected.
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And we have had several guests even on this show when we spoke about ethical AI and ethics in general when it comes to machine learning and algorithms.
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Whenever they are applied to all these critical applications where there is a lot of human involvement.
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Think about healthcare.
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Pharmaceuticals.
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Autonomous vehicles that have to decide to stop or kill the dog or the person in extremely critical situations.
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Or think about defense and military.
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Whenever you deploy artificial intelligence in all these critical sectors that have to do with humans, well, then we should be thinking about ethical algorithms, that’s for sure.
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So we have seen this happening.
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One thing that, however, I didn’t expect personally is that industry is following a very similar trend.
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And so that rarely happens, to be honest, that education and industry go hand in hand on certain matters.
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And we have seen that in 2018 to 2021, there has been pretty much a similar growth in terms of number of papers for the ethical sector, even in the industry.
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So this tells a good story, because probably already the idea of thinking about ethical algorithms is also something that people from industry are considering as a source of problems.
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If we don’t deal with these issues, we better deal with these issues now, because algorithms are more and more penetrating the daily life of people, and many other fields out there are pretty much tainted by artificial intelligence.
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So we better start thinking of ethical AI on a much more serious level.
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Now, when it comes to the people involved in AI, the people who do AI, well, there’s a different story, a pretty depressing one, I must say.
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Only a couple of points, percentage points when it comes to AI and women from 2010.
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So ten years ago, twelve years ago, we had something like 16, probably 17% females and computer science PhDs.
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That’s the percentage represented in North America.
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And in 2020, only a couple of percentage points.
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That’s really depressing.
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Like only a couple of percentage points.
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AI definitely needs women and this is not happening.
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So we better insist on this.
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I don’t accept this.
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I honestly don’t accept this also because at the same time we have seen, again in the same demographics in North America, we have seen an increase in number of computer science undergraduates and graduates at doctoral institutions, and that’s been a massive increase.
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So if you combine these two numbers and these two trends here, the story is pretty clear.
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Even the new computer science undergraduates and graduates are mostly males.
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And that’s also something that it’s hard to swallow.
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I don’t like that.
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I think I said that a number of times already.
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We need more women in AI.
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Not only that, we also need different ethnic backgrounds to be represented in a more homogeneous way.
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Personally, I find it unacceptable to have almost 60% white non Hispanic PhDs and CS undergraduates in AI.
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The Asian population is represented only for 25%, but Hispanic and black or African American are represented for merely 3.5%.
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So that’s also unacceptable.
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And the reason why I’m saying that is because there might be bias in the data, as we have proved many times, but there are also bias in research.
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And so if only a particular ethnic background is representative of the community, of the AI community, I believe that their research directions will also be biased.
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And that’s also something that doesn’t sound right to me.
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Now, last but not least, and this is also something that kept me awake at night, is climate change.
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We have claimed climate change to be the next big sector in which we should have seen, or we would have liked to see AI in action.
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And that’s not happening now.
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Of course, 2022 is not finished yet, but the trend is quite depressive as well.
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So I have a list of the topics where AI related papers and policy papers in particular are most represented.
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And we have at the top of the list, of course, privacy, safety and security.
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We have innovation and technology.
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We have ethics, as we have already said, and then we have all the others that go down and down, like industry and regulation, workforce and label, education and skills, et cetera, et cetera.
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At the end of that list, and again I will send you the link of this chart in the show notes of this episode.
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At the end of this list, we have social and Behavioral Sciences, health and Biological sciences, energy and Environment, and Humanity.
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So these are the last two in the list, energy and Environment.
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So I definitely do not.
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Did not want to see Energy and Environment last month before the last in that list.
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Now.
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Again.
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What my speculation is about.
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And because I don’t have the correct breakdown or the detailed breakdown of these findings.
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I expect that some of the innovation that is related to energy and environment is probably grouped in the innovation and technology field.
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Which is second in the list.
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But I’m not sure about that.
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That’s why we need more detailed results.
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But no matter what we should definitely think about this.
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Energy and environment is probably one of the probably the second most important topic that we should be covering together with health care and of course, safety in general, and where I believe that artificial intelligence can play a fundamental role when it comes to energy, renewable energy, and of course, the environment in general.
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That’s it for today.
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I hope you enjoyed the show.
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Speak with you next time.
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You’ve been listening to Data Science at home podcast.
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Be sure to subscribe on itunes, Stitcher or Pod Bean to get new, fresh episodes.
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