Category Archives: Alphago
In January 2020, Tony Seba gave the keynote speech at the North Carolina Department of Transportation 2020 Summit.
He titled his speech "The Future of Transportation."
It is a very good speech. If you have an hour to watch it, I recommend doing so. Or, you can just read my summary and get the key points he made. This summary is not a transcript. I have quoted some of what he said, but mostly I've organized and paraphrased his words.
Tony begins talking in general about technology disruption. He begins with the adoption of automobiles. He showed a picture of the 1900 Easter parade in New York City. The road was filled with horse-drawn carriages and one automobile. He then showed a picture taken in 1913 in New York City. The road was filled with automobiles and one horse-drawn carriage.
He gives the following definition:
"A disruption is essentially when there is a convergence of technologies that make a product or service possible, and that product, in turn, can help create new markets ... and at the same time destroy or radically transform existing industries."
Tony explains a few case studies of companies that have experienced disruptions. These disruptions were often glossed over by analysts. He cites AT&T and Nokia.
He asks, why do smart organizations fail to anticipate or lead in technology disruptions? He talks a little about his disruption framework, which he has created to examine disruptions. He asks, "can we anticipate, can we forecast, more or less, disruptions to come?"
Tony takes a dive into technology cost curves. As production expands, the costs of technologies come down. Technologies are not adopted linearly but are always adopted in an S curve manner.
He reflects on the speed of the automobile adoption. It went from 0% to 95% nationally in 20 years. However, the adoption went from the tipping point to 80% in just 10 years. All the while, the US concurrently built the oil industry (distribution infrastructure), a national road infrastructure and fought WWI.
Tony touts that technology S curves are getting steeper. Adoption is happening faster and faster.
Analysts' projections are very often linear. Analysts often don't take into consideration the fact that adoptions are about systems, technology improvements working together. He cites forecasts made by the EIA (Energy Information Administration) as an example. The EIA has consistently failed with many projections.
Tony talks about technology convergence, a set of technologies that come together at the same time. He says disruptions happen from the outside. It's very rare that a company disrupts itself.
He briefly discusses ride-hailing, Uber & Lyft. The smart-phone made ride-hailing possible. In just eight years ride-hailing went from 0% to 20% of vehicle miles driven in San Francisco. He predicts that in 2020, we will realize peak new-car (globally). Car ownership will begin to decline thereafter.
Tony talks about the concept of "Market Trauma." Mainstream analysts say that EVs are "only 2% - 3% of the market, how much damage can it do?" They say "it's going to take 10 - 20 years before this takes over." A technology can disrupt the economics of an incumbent way before they have 10 - 20% of the market.
Small changes can have swift, dramatic impact in existing industries. In 2014, Tony wrote a book called "Clean Disruption of Energy & Transportation." The book focuses on these areas; batteries, electric vehicles, autonomous vehicles, on-demand transportation and solar.
In 2014, he made a predictive cost curve for lithium-ion batteries. He predicted that lithium-ion batteries would cost $100/kWh by 2023. His cost curve has actually proven to be a little conservative. Tony gives batteries storage as an example of "Market Trauma."
One example is the Tesla battery bank in Australia. The Tesla battery holds only 2% of the market capacity (ancillary services market) and yet has taken 55% market share. It has pushed down wholesale prices by 90%. Incumbent revenues have been brought down by 90%. Natural gas peaker-plants are being stranded. He also cites GE's mistaken choice to invest heavily in natural gas electrical as another example of market trauma.
Tony transitions to talking about EVs. He talks about the "gas savings EVs enjoy. EVs are much cheaper to operate. They are up to ten times cheaper to maintain. He shows a clip of the Rivian truck doing tank turns, as an example of EVs being a better product.
EVs have a much longer life span, up to 500,000 miles, up to 2.5 times longer than IC vehicles. This is of particular interest to fleet operators. It makes total sense for fleet managers to go full EV.
He shows his cost curve for EVs. He predicts in his curve that basic 200 mile EVs will cost as little as $12,000 by 2025.
Tony predicts that next year is the EV tipping point, "for purely economic reasons." He says "it won't make any sense to buy a gas car." He predicts that every new car after 2025 will be electric. Tony cites Amazon's order of 100,000 delivery vans from Rivian, "for purely economic reasons."
Tony goes on to talk about autonomous technology. He features Waymo's autonomous ride-hailing. More than four dozen companies are investing in autonomous technology. He says "Think of EVs as computers on wheels." No one is waiting around to create autonomous technology. Only two companies will survive (only two autonomous companies).
Autonomous vehicles are safer than humans. Prediction: by 2030, we are going to be talking about taking away drivers licenses from humans. Insurance costs for human drivers will go up.
Tony brings up computing power. How quickly is the supercomputing cost curve improving? In the year 2000, the largest supercomputer on earth cost 46 million dollars and could do 1 TeraFlops (Sandia National Labs). The Apple X on iPhone released in 2019 can do 5 TeraFlops and costs about $600.
The improvements in AI are double exponential. Tony cites an AI learning to play AlphaGo and beating the world champion, then the next generation AI learning to beat the previous generation AI within days rather than months or years.
The real big disruption is in the convergence of electric vehicles, ride-hailing and autonomous vehicles. This convergence will create Transportation as a Service (TaaS). "Everyone is going electric." DIDI (the China equivalent to Uber) expects to have 1 million electric vehicles on the road by 2020.
Tony predicts that when autonomous technology goes live (approved) consumers will face the choice of buying a vehicle or using TaaS. TaaS will be up to ten times less expensive than vehicle ownership. Tony predicts that eventually, TaaS will cost less than 18 cents per mile. He says that by 2030 95% of all vehicles miles driven will be done by TaaS fleets.
By 2030 vehicle ownership will be 60% fleets and 40% personal. However, most of the miles will be driven by TaaS fleets. People will save, on average, $5,600 a year. The total US vehicle fleet will shrink by 70%. There will be fewer cars. Those fewer cars will be doing most of the driving miles.
By 2030 Taas will save the economy 1 trillion dollars per year. US disposable income will increase by 1 trillion dollars per year. The cost of travel will be only 5 cents to 10 cents per mile. This will have implications for the economy, social, health, work and other areas.
Tony predicts that oil demand will peak this year or next (2021). After this, the price of oil will eventually fall to $25 per barrel.
He talks about parking lots. He says 80% of parking will become obsolete. There will be a drastically reduced need for parking lots. That space can be re-utilized. It can be used for other things.
Tony says this disruption is not just about transportation, everything is changing. Now is the time to imagine what type of city we want in 10 years. He says that it is as if we are in 1900, we are on the cusp of the deepest, fastest, most consequential disruption in 100 years, and perhaps ever.
From the comments on the video, Tony has been saying these things for a while. It is of note, though, how closely he has come to the mark.
I question the 10 cents per mile for ride-hailing and 70% fewer automobile ownership. For the ride-hailing to be this low, electricity would have to be very cheap, and the cost of the autonomous vehicles would have to be extremely low. If an autonomous vehicle was available for $20,000 and it could go 1 million miles with minimal maintenance, then the amortized cost could be 2 - 3 cents per mile. Add to that electricity, at least 4 cents a mile. Add to that the ride-hailing service's cut and the total is going to be over 10 cents a mile.
Besides, if I could buy a million-mile EV for $20,000, why wouldn't I? It could be the last car I'd ever have to buy. The low-cost EV that makes the cheap ride-hailing possible also makes cheap automobile ownership possible, a bit of a paradox (sounds like the topic for another article). I guess we have to wait only a few years to see if Tony Seba is correct.
The Future of Transportation, Tony Seba Keynote Speaker at the 2020 NC DOT Summit http://www.youtube.com/watch?v=y916mxoio0E
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2020: The Year Of Peak New-Car? Disruption Is Fast Approaching - InsideEVs
AI on steroids: Much bigger neural nets to come with new hardware, say Bengio, Hinton, and LeCun – ZDNet
Geoffrey Hinton, center. talks about what future deep learning neural nets may look like, flanked by Yann LeCun of Facebook, right, and Yoshua Bengio of Montreal's MILA institute for AI, during a press conference at the 34th annual AAAI conference on artificial intelligence.
The rise of dedicated chips and systems for artificial intelligence will "make possible a lot of stuff that's not possible now," said Geoffrey Hinton, the University of Toronto professor who is one of the godfathers of the "deep learning" school of artificial intelligence, during a press conference on Monday.
Hinton joined his compatriots, Yann LeCun of Facebook and Yoshua Bengio of Canada's MILA institute, fellow deep learning pioneers, in an upstairs meeting room of the Hilton Hotel on the sidelines of the 34th annual conference on AI by the Association for the Advancement of Artificial Intelligence. They spoke for 45 minutes to a small group of reporters on a variety of topics, including AI ethics and what "common sense" might mean in AI. The night before, all three had presented their latest research directions.
Regarding hardware, Hinton went into an extended explanation of the technical aspects that constrain today's neural networks. The weights of a neural network, for example, have to be used hundreds of times, he pointed out, making frequent, temporary updates to the weights. He said the fact graphics processing units (GPUs) have limited memory for weights and have to constantly store and retrieve them in external DRAM is a limiting factor.
Much larger on-chip memory capacity "will help with things like Transformer, for soft attention," said Hinton, referring to the wildly popular autoregressive neural network developed at Google in 2017. Transformers, which use "key/value" pairs to store and retrieve from memory, could be much larger with a chip that has substantial embedded memory, he said.
Also: Deep learning godfathers Bengio, Hinton, and LeCun say the field can fix its flaws
LeCun and Bengio agreed, with LeCun noting that GPUs "force us to do batching," where data samples are combined in groups as they pass through a neural network, "which isn't efficient." Another problem is that GPUs assume neural networks are built out of matrix products, which forces constraints on the kind of transformations scientists can build into such networks.
"Also sparse computation, which isn't convenient to run on GPUs ...," said Bengio, referring to instances where most of the data, such as pixel values, may be empty, with only a few significant bits to work on.
LeCun predicted they new hardware would lead to "much bigger neural nets with sparse activations," and he and Bengio both emphasized there is an interest in doing the same amount of work with less energy. LeCun defended AI against claims it is an energy hog, however. "This idea that AI is eating the atmosphere, it's just wrong," he said. "I mean, just compare it to something like raising cows," he continued. "The energy consumed by Facebook annually for each Facebook user is 1,500-watt hours," he said. Not a lot, in his view, compared to other energy-hogging technologies.
The biggest problem with hardware, mused LeCun, is that on the training side of things, it is a duopoly between Nvidia, for GPUs, and Google's Tensor Processing Unit (TPU), repeating a point he had made last year at the International Solid-State Circuits Conference.
Even more interesting than hardware for training, LeCun said, is hardware design for inference. "You now want to run on an augmented reality device, say, and you need a chip that consumes milliwatts of power and runs for an entire day on a battery." LeCun reiterated a statement made a year ago that Facebook is working on various internal hardware projects for AI, including for inference, but he declined to go into details.
Also: Facebook's Yann LeCun says 'internal activity' proceeds on AI chips
Today's neural networks are tiny, Hinton noted, with really big ones having perhaps just ten billion parameters. Progress on hardware might advance AI just by making much bigger nets with an order of magnitude more weights. "There are one trillion synapses in a cubic centimeter of the brain," he noted. "If there is such a thing as General AI, it would probably require one trillion synapses."
As for what "common sense" might look like in a machine, nobody really knows, Bengio maintained. Hinton complained people keep moving the goalposts, such as with natural language models. "We finally did it, and then they said it's not really understanding, and can you figure out the pronoun references in the Winograd Schema Challenge," a question-answering task used a computer language benchmark. "Now we are doing pretty well at that, and they want to find something else" to judge machine learning he said. "It's like trying to argue with a religious person, there's no way you can win."
But, one reporter asked, what's concerning to the public is not so much the lack of evidence of human understanding, but evidence that machines are operating in alien ways, such as the "adversarial examples." Hinton replied that adversarial examples show the behavior of classifiers is not quite right yet. "Although we are able to classify things correctly, the networks are doing it absolutely for the wrong reasons," he said. "Adversarial examples show us that machines are doing things in ways that are different from us."
LeCun pointed out animals can also be fooled just like machines. "You can design a test so it would be right for a human, but it wouldn't work for this other creature," he mused. Hinton concurred, observing "house cats have this same limitation."
Also: LeCun, Hinton, Bengio: AI conspirators awarded prestigious Turing prize
"You have a cat lying on a staircase, and if you bounce a soccer ball down the stairs toward a care, the cat will just sort of watch the ball bounce until it hits the cat in the face."
Another thing that could prove a giant advance for AI, all three agreed, is robotics. "We are at the beginning of a revolution," said Hinton. "It's going to be a big deal" to many applications such as vision. Rather than analyzing the entire contents of a static image or video frame, a robot creates a new "model of perception," he said.
"You're going to look somewhere, and then look somewhere else, so it now becomes a sequential process that involves acts of attention," he explained.
Hinton predicted last year's work by OpenAI in manipulating a Rubik's cube was a watershed moment for robotics, or, rather, an "AlphaGo moment," as he put it, referring to DeepMind's Go computer.
LeCun concurred, saying that Facebook is running AI projects not because Facebook has an extreme interest in robotics, per se, but because it is seen as an "important substrate for advances in AI research."
It wasn't all gee-whiz, the three scientists offered skepticism on some points. While most research in deep learning that matters is done out in the open, some companies boast of AI while keeping the details a secret.
"It's hidden because it's making it seem important," said Bengio, when in fact, a lot of work in the depths of companies may not be groundbreaking. "Sometimes companies make it look a lot more sophisticated than it is."
Bengio continued his role among the three of being much more outspoken on societal issues of AI, such as building ethical systems.
When LeCun was asked about the use of factual recognition algorithms, he noted technology can be used for good and bad purposes, and that a lot depends on the democratic institutions of society. But Bengio pushed back slightly, saying, "What Yann is saying is clearly true, but prominent scientists have a responsibility to speak out." LeCun mused that it's not the job of science to "decide for society," prompting Bengio to respond, "I'm not saying decide, I'm saying we should weigh in because governments in some countries are open to that involvement."
Hinton, who frequently punctuates things with a humorous aside, noted toward the end of the gathering his biggest mistake with respect to Nvidia. "I made a big mistake back in with Nvidia," he said. "In 2009, I told an audience of 1,000 grad students they should go and buy Nvidia GPUs to speed up their neural nets. I called Nvidia and said I just recommended your GPUs to 1,000 researchers, can you give me a free one, and they said no.
"What I should have done, if I was really smart, was take all my savings and put it into Nvidia stock. The stock was at $20 then, now it's, like, $250."
When people hear about the race for Artificial Intelligence (AI) dominance, they often think that the main competition is between the US and China. After all, the US and China have most of the largest and most well funded AI companies on the planet, and the pace of funding, company growth, and adoption doesnt seem to be slowing anytime soon. However, if you look closely, youll see that many other countries have a stake in the AI race, and indeed, some countries have AI efforts, funding, technologies, and intellectual property that make them serious contenders in the jostling for AI dominance. In fact according to a recent report from analyst firm Cognilytica, France, Israel, United Kingdom, and the United States all are equally strong when it comes to AI, with China, Canada, Germany, Japan, and South Korea equally close in their AI strategic strength. (Disclosure: Im a principal analyst with Cognilytica).
The Current Leaders in AI Funding and Dominance: US and China
AI startups are raising more money than ever. AI-focused companies raised $12 Billion in 2017 alone, more than doubling venture funding over the previous year. Most of this funding is concentrated in US and Chinese companies, but the source of those funds is much more international. Softbank, based in Japan, has amassed a $100 Billion investment fund, with many international investors including Saudi Arabias sovereign investment fund and other global sources of capital. While US companies have put up significant investment rounds with the power of Silicon Valleys VC funds, China now has the most valuable AI startup, Sensetime, which raised over $1.2 Billion and a rumored additional $1 Billion raise on the way.
However, what makes AI as a technology sector different from previous major waves of investment, is that AI is seen as strategic technology by many governments. In 2017 China released a three step program outlining its goal to become a world leader in A.I. by 2030. The government aims to make the AI industry worth about $150 billion and is pushing for greater use of AI in a number of areas such as the military and smart cities. Furthermore, the Chinese government has made big bets including a planned $2.1 Billion AI-focused technology research park. And in 2019 TheBeijing AI Principleswere released by a multistakeholder coalition including the Beijing Academy of Artificial Intelligence (BAAI), Peking University, Tsinghua University, Institute of Automation and Institute of Computing Technology in Chinese Academy of Sciences, and an AI industrial league involving firms like Baidu, Alibaba and Tencent.
In addition, the Chinese technology ecosystem has developed to become a powerhouse in its own right. China has many multi-billion dollar tech giants including Alibaba, Baidu, Tencent, and Huawei Technologies, who are each heavily investing in AI. Chinese companies also work more closely with the Chinese government, and laws in China are the most relaxed with regards to customer privacy and use of AI technologies such as facial recognition on their citizens. Chinas government has already embraced the use of facial recognition technology and has quickly adopted this technology in everyday use. In most other counties such as the US for example, privacy concerns prevent pervasive use of facial recognition technology, but such concerns or impediments to adoption dont exist in China.
The story of technology company creation and funding in the United States is already well known. Silicon Valley is both a region as well as a euphemism for the entire tech industry, showing how dominant the US has been for the past several decades with technology creation and adoption. Venture capital as an industry was invented and perfected in the US, and the result of that has been the creation of such enduring tech giants like Amazon, Apple, Facebook, Microsoft, Google, IBM and thousands of other technology firms big and small. Collectively trillions of dollars has been invested in these firms by private and public sector investors to create the technology industry as we know it today. Certainly, none of that is going away anytime soon.
In addition, the US has an extremely well developed and highly skilled labor pool with academic powerhouses and research institutions that continue to push the boundaries of what is capable with AI. What is notable is that even in the US, the dominance of Silicon Valley as a specific, San Francisco-bay geographic region is starting to slip. The New York city region has produced many large AI-focused technology firms, and research in the Boston-area centered around MIT and Harvard, Pittsburgh with Carnegie Mellon, the Washington, DC metro area with its legions of government-focused contractors and development shops, Southern Californias emerging tech ecosystem, Seattle-based Amazon and Microsoft, and many more locations in the US are loosening the hold that Northern California has on the technology industry with respect to AI. And just outside the US, Canadian firms from Toronto, Montreal, and Vancouver are further eroding the dominance of Silicon Valley with respect to AI.
In 2018 the United States issued an Executive Order from the President naming AI the second highest R&D priority after the security of the American people for the fiscal year 2020. Additionally, the U.S. Department of Defenseannouncedit will invest up to $2 billion over the next five years towards the advancement of AI. As recently as 2020 the United States launched the American AI Initiative with the strategy aimed at focusing the federal government resources. The US federal government also launched AI.gov to make it easier to access all of the governmental AI initiatives currently underway. Once potentially seen lackluster in comparison to that of China and other countries the US government has really started making AI a priority to keep up in recent years.
Countries With Significant Stakes in AI
As mentioned above, what makes the AI industry unique is that it is actually not a new thing, but rather evolved over decades, even prior to the development of the modern digital computer. As a result, many technology developments, investment, and intellectual property exists outside the US and China. Countries that have been involved with AI since the early days are realizing the strategic nature of AI and doubling down on their efforts to retain a stake in global AI share and maintain their relevance and importance.
Japan has long been a leader in the AI industry, and in particular their development and adoption of robotics. Japanese firms introduced concepts such as the 3 Ds (Ks) of robotics that we discussed in our research on cobots. Not only is their technology research excellence on par with anywhere in the world, they have the funding to back it up. As mentioned earlier, Japan-based Softbank is an investor powerhouse unrivaled in the venture capital industry.
Japans government released their Artificial Intelligence Technology Strategy in March 2017. This strategy includes an Industrialization Roadmap and focuses the development of AI into three phases: the utilization and application of AI through 2020, the publics use of AI from 2025-2030, and lastly an ecosystem built by connecting multiplying domains. The countrys strategy focuses on R&D for AI, collaboration between industry, government, and academia to advance AI research, and addressing areas related to productivity, welfare and mobility.
However, it is important to note that while Japan continues to exhibit dominance in robotics and other AI fields as well as its Softbank powerhouse, many of the firms that Softbank is investing in are not Japan-based, and so much of the investment is not remaining focused on Japans own AI industry. In addition, while technology development is advanced and rapidly progressing and while Japan is known as a country to embrace technology, many Japanese companies have not been quick to embrace AI technology and the use of AI is largely limited to the financial sector and concentrated in the manufacturing industry. The country is also facing significant demographic pressure, with an aging population, causing a shortage in available workforce. On the one hand, the adoption of AI and robotic technologies are seen as a solution to labor and aging demographics, on the otherhand, the lack of workforce will cause strategic problems for creation of AI dominant companies.
South Koreas government is a significant investor and strong supporter of local technology development, and AI is certainly no exception. The government recently announced it plans to spend $2 billion by 2022 to strengthen its AI R&D capability including creating at least six new AI schools by 2020, with plans to educate more than 5,000 new high quality engineers in Korea in response to a shortage of AI engineers. The government also plans to fund large scale AI projects related to medicine, national defense, and public safety as well as starting an AI R&D challenge similar to those developed by the US Defense Advanced Research Projects Agency (DARPA). The government will also invest to support the creation and development of AI startups and businesses. This support includes the creation of an AI-oriented start-up incubator to support emerging AI businesses and funding for the creation of an AI semiconductor by 2029.
South Korea is home to many large tech companies such as Samsung, LG, and Hyundai among others, and is known for its automotive, electronics, and semiconductor industries as well as the use of industrial robotics technology. It also famously hosted the match where DeepMinds AlphaGo defeated Gos world champion Lee Sedol (a Korean-native). Clearly, you cant count South Korea out of any race for AI dominance. The only thing significantly lacking is a well-developed venture capital ecosystem and a large number of startups. South Koreas AI efforts are almost entirely concentrated in the activities of the major technology incumbents and government activities.
The United Kingdom is a clear leader for AI and the government is financially supporting AI initiatives. In November 2017, the UK government announced 68 million of funding for research into AI and robotics projects aimed at improving safety in extreme environments as well as funding four new research hubs that will be created to help develop robotic technology to improve safety in off-shore wind and nuclear energy. It has a goal to invest about $1.3 billion in AI investment from both public and private funds over the coming years. As part of this plan, Global Brain, a Japan-based venture capital firm, plans to invest about $48 million in AI-focused UK-based tech startups as well as open a European headquarters in the United Kingdom. Canadian venture capital firm Chrysalix also plans to open a European headquarters in the U.K. as well as invest over $100 million in UK-based startups who specialize in AI and robotics. The University of Cambridge is installing a $13 million supercomputer and will give U.K. businesses access to the new supercomputer to help with AI-related projects.
The U.K. is of course also the home of Alan Turing, renowned forefather of computing and an early proponent of AI, with the namesake Turing Test. The UK can also claim (in not such a great light) to be one of the precipitating factors of the first AI Winter when the Lighthill Report was released in 1973 leading to significant declines in AI investment. As such, the UK has exhibited in the past significant influence positively, and negatively, in worldwide AI spending and adoption. To avoid future problems, the U.K. is looking to position itself as a world leader in ethical AI standards. The UK sees this as an opportunity to position itself as an AI leader with ethical AI, helping to create standards used for all. It knows it cant compete with AI funding and development from counties like the US and China but thinks it has a shot by taking an ethical standards approach and leveraging its early status as a lead in AI development.
Frances President Emmanuel Macron released a national strategy for artificial intelligence in early 2018. The country announced that over the next five years it will invest more than 1.5 billion for AI-related research and support for emerging startups in a bid to compete with the US, China, and others for AI dominance. The French strategy is to put an emphasis on and target four specific areas of AI related to health, transportation (such as driverless cars), the environment, and defense/security. Some notable AI researchers and data scientists were educated in France, such as Facebooks head of AI Yann LeCun. France wants to try to keep that talent in France instead of moving to overseas companies.
Many companies such as Samsung, Fujitsu, DeepMind, IBM and Microsoft have announced plans to open offices in France for AI research. The French administration also wants to share new data sets with the public making it easy to access and build AI services using those data sets. The caveat to receiving public funds is that research projects or companies financed with public money will have to share their data. Many European Union (EU) officials have expressed dismay with the way that Facebook, Google, Microsoft, Amazon, and others have hoarded user data, and Macron and his administration are concerned about the black box of AI data and decision-making. France is also focused on addressing the ethical concerns around AI as well as trying to create unbiased data sets which is part of the reason for open algorithms and data sets. While Frances efforts are significant, they pale in terms of total money put into the industry and resources available to compete with the efforts of other nations.
Germany is an industrial powerhouse, has long been known to have great engineering capabilities, and Berlin is currently Europes top AI talent hub. According to Atomicos 2017 State of European Tech report, Germany is most likely to become a leader in areas such as autonomous vehicles, robotics and quantum computing. In fact, almost half of all worldwide patents on autonomous driving come from German car companies or their suppliers such as Bosch, Volkswagen, Audi and Porsche. These German companies had begun their autonomous vehicle development activities as early as 1986.
A new tech hub region in southern Germany, called Cyber Valley, is hoping to create new opportunities for collaboration between academics and businesses with a specific focus on AI. The new hub plans to focus on AI and robotics, make better use of research talent, and collaboratively work with companies such as Porsche, Daimler and Bosch. In addition to autonomous vehicles, Germany has an early lead with robotics, with one of the first cobots developed in Germany for use in manufacturing. Additionally, Germanys AI strategy was published in December 2018 in Nuremberg. And, in 2019, The German government tasked a new Data Ethics Commission with producing guidelines for the development and use of AI.
Despite these intellectual property and early market leads, Germany has not invested at the same levels as other countries, and the technology firms are highly concentrated in manufacturing, automotive, and industrial sectors, leaving other markets mostly untapped with AI capabilities. Furthermore, American automakers such as Ford, GM, and Google Waymo, as well as Uber and other firms are quickly catching up with the number of patents issued and threatening Germanys dominance for intellectual property in that area.
Russian president Vladimir Putin made a statement that: Artificial intelligence is the future, not only for Russia, but for all of humankind and that whichever country becomes the leader in this sphere will become the ruler of the world. This is one powerful statement. Russia has said that intelligent machines are vital to the future of their national security plans and, by 2025, it plans to make 30% of its countrys military equipment robotic. The government also wants to standardize development of artificial intelligence focusing on image recognition, speech recognition, autonomous military systems, and information support for weapons life-cycle. There is also a new Russian AI Association bringing the academic and private-sector together. Additionally, Russian President Vladimir Putin approved the National Strategy for the Development of Artificial Intelligence (NSDAI) for the period until 2030 on October 2019.
Russia is still a world superpower in terms of military might, and exerts significant influence in world markets, especially in the energy sector. Despite that, Russian investment in AI is still significantly lacking that of other countries, with only a reported $12M invested by the government in research efforts. While Russia has had significant input and efforts around AI research in the university setting, the countrys industry lacks overall AI talent and number of companies working towards AI related initiatives. Many skilled Russian engineers leave the country to work at other firms worldwide who are throwing lots of money at skilled talent. As such, the biggest application of AI in Russia is in physical and cyberwarfare situations, leveraging AI to enhance the capabilities of autonomous vehicles and information warfare. In this arena, Russia is certainly a country to be contended with regards to AI dominance.
Other AI Hotspots
In addition to the above, there are many countries that are seeing AI as a country level strategic initiative including Israel, India, Denmark, Sweden, Estonia, Finland, Netherlands, Poland, Singapore, Malaysia Australia, Italy, Canada, Taiwan, the United Arab Emirates (UAE), and other locations. Some of these countries have more financial than technical resources, or vice-versa. The key is that for each of these countries, they see AI in a strategic light and as such theyve crafted a strategic approach to AI.
AI technologies have the ability to transform and influence the lives of many people. Not only will AI transform the way we work, interact with each other and travel between locations, but it also has an impact on weapons technology, modern warfare, and a countrys cyber security. AI can also have a dramatic impact on the labor market, disrupting entire industries and creating whole new ones. As such, having a focus on AI dominance can also help strengthen that countrys economy, shift global leadership and power, and give military advantages. While the race for AI domination might seem similar to the Space Race or aspects of the Cold War, in reality the AI market doesnt support a winner take all approach. Indeed, continued advancement in AI requires research and industry collaboration, continued research and development, and industry-wide thinking and solutions to problems. While there will no doubt be winners and losers in terms of overall investment and return, countries worldwide will reap the benefits of increased adoption and development of cognitive technologies.
So Is an AI Winter Really Coming This Time? – Walter Bradley Center for Natural and Artificial Intelligence
AI has fallen from glorious summers into dismal winters before. The temptation to predict another such tumble recurs naturally. So that is the question the BBC posed to AI researchers: Are we on the cusp of an AI winter:
The 10s were arguably the hottest AI summer on record with tech giants repeatedly touting AIs abilities.
AI pioneer Yoshua Bengio, sometimes called one of the godfathers of AI, told the BBC that AIs abilities were somewhat overhyped in the 10s by certain companies with an interest in doing so.
There are signs, however, that the hype might be about to start cooling off.
I keep up with this kind of thing. The answer is: Yes, and no. AI did surge past milestones during the 2010s that it had not been expected to cross for many more years:
2011 IBMs Watson wins at Jeopardy! IBM Watson: The inside story of how the Jeopardy-winning supercomputer was born, and what it wants to do next (Tech Republic, September 9, 2013)
2012 Google unveils a deep learning systems that recognized images of cats
2015 Image recognition systems outperformed humans in the ImageNet challenge
2016 AlphaGo defeats world Go champion Lee Sedol: In Two Moves, AlphaGo and Lee Sedol Redefined the Future (Wired, March 16, 2016)
2018 Self-driving cars hit the road as Googles Waymo launched (a very limited) self-driving taxi service in Phoenix, Arizona
But other headlines during the period have been less heeded:
Despite High Hopes, Self-Driving Cars Are Way in the Future (2019)
The Next Hot Job: Pretending to Be a Robot (2019)
Boeings Sidelined Fuselage Robots: What Went Wrong? (2019)
Self-driving cars: Hype-filled decade ends on sobering note (2019)
Tesla driver killed in crash with Autopilot active, NHTSA investigating (2016)
Dont fall for these 3 myths about AI, machine learning (2018)
A Sobering Message About the Future at AIs Biggest Party (2019)
And so on.
So which is it? AI Winter or Robot Overlords? I suggest neither. And so do active researchers.
Gary Marcus, an AI researcher at New York University, said: By the end of the decade there was a growing realisation that current techniques can only carry us so far.
He thinks the industry needs some real innovation to go further.
There is a general feeling of plateau, said Verena Rieser, a professor in conversational AI at Edinburgh[s Heriot Watt University.
One AI researcher who wishes to remain anonymous said were entering a period where we are especially sceptical about AGI.
Recent AI developments, notably those lumped under the rubric of Deep Learning have advanced the state-of-the-art in machine learning. Lets not forget that prior efforts, such as the poorly named Expert Systems, had faded because, well, they werent expert at all. Deep Learning systems, as highly flexible pattern matchers, will endure.
What is not coming is the long-predicted AI Overlord, or anything that is even close to surpassing human intelligence. Like any other tool we build, AI has its place when it amplifies and augments our abilities.
Just as tractors and diggers have not led to legions of people who no longer use their arms, the latest advances in AI will not lead to human serfs cowering before beneath an all-intelligent machine. If anything, AI will require more from us, not less, because how we choose to use these tools will make an increasingly stark difference between benefit and ruin.
As Samin Winiger, a former AI research at Google says, What we called AI or machine learning during the past 10-20 years, will be seen as just yet another form of computation
Machines are tool in the toolbox, not a replacement for minds. An AI winter would only be coming if we forgot that.
Here are some of Brendan Dixons earlier musings on the concept of an AI Winter:
Just a light frost? Or an AI winter? Its nice to be right once in a whilecheck out the evidence for yourself
AI WinterIs Coming:Roughly every decade since the late 1960s has experienced a promising wave of AI that later crashed on real-world problems, leading to collapses in research funding.