Category Archives: Ai

Founders Factory, the corporate accelerator vehicle set up by Brent Hobermans Founders Forum umbrella, has today announced the first two startups selected for their AI accelerator programme in conjunction with CSC Group. The idea is to co-create two new AI businesses within the incubator programme every year, for five years. Terms were not disclosed.

CSC Group invested in Founders Factory (co-founded with Henry Lane Fox) in October 2016. CSC is one of Chinas biggest tech investors they put $400m into Angelist 18 months ago. Their idea is to provide a bridge for these startups to later enter the Chinese market.

The first two startups being announced for the AI accelerator programme are Iris.ai. The AI-driven research assistant helps users to search and map over 60 million open access research papers, doubling the productivity of research teams. Founded by Anita Schjll Brede (CEO), Maria Ritoia (CMO) and Jacobo Elosua (CFO) in 2015, Iris.ai launched at TechCrunch Disrupt in London last year.

The second is illumr. Founded by Jason Lee (CEO) in 2013, and with a decade of research behind it, this helps organisations better understand and predict patterns of behaviour that affect them. illumr turns complex datasets into understandable 3D patterns to reveal insights that all other analytical tools and methodologies may miss. So far, illumr has worked with government departments, large financial institutions and housing organisations.

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Founders Factory invests in its first two A.I. startups Iris.ai and illumr - TechCrunch

Style transfer has suddenly become a hot thing, apparently, as Adobe recently showed off an experimental app that lets you apply one photo style ('90s stoner landscapes) to another (your crappy smartphone photo).

UC Berkely researchers have taken that idea in another direction. You can take, for instance, a regular photo and transform it into a Monet, Van Gogh, Cezanne or Ukiyo-e painting. The team was also able to use the technique to change winter Yosemite photos into summer ones, apples into (really weird) oranges and even horses into zebras. The technique also allowed them to do photo tricks like creating a shallow depth of field behind flowers and other objects.

The most interesting aspect of the research is the fact that the team used what's called "unpaired data." In other words, they don't have a photo taken at the scene at the exact moment Monet did his painting. "Instead, we have knowledge of the set of Monet paintings of of the set of landscape photographs. We can reason about the stylistic differences between those two sets, and thereby imagine what a scene might look like if we were to translate it from one set into another."

That's easier said than done though. First, they needed to figure out the relationships between similar styles in a way that a machine can understand. Then they trained so-called "adversarial networks" using a large number of photos (from Flickr and other sources) and refined them by having both people and machines check the quality of the results.

Ideally, the system would be "cycle consistent." Just as you hope to have the original sentence when you translate English to French and back again, you want roughly the same painting when you translate a Monet to a photo and back again. In many cases, other than a loss of pixel resolution, the team succeeded in that regard (above).

All is not perfect, of course. Since the algorithms have to deal with a lot of different styles for both paintings and photos, they often fail completely to transfer one to another. As with other systems, one of the main issues is with geometric transformations -- changing an apple into an orange is one thing, but attempting to transform a cat into a dog instead produces a very disturbing cat.

The team adds that its methods still aren't as good as using paired training data either -- ie, photos that exactly match paintings. Nevertheless, left on its own accord, the AI is surprisingly good at transferring one image style to another, so you'll no doubt see the results of their work soon in your Instagram feed. If you want to try it for yourself and are comfortable with Linux, you can grab the code here.

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'Reverse Prisma' AI turns Monet paintings into photos - Engadget

Whats the first thing that comes to mind when you hear artificial intelligence? For those raised on a steady diet of big budget Hollywood sci-fi, the answer to that question is something along the lines of evil robots and all-knowing computers that are going to destroy humanity.

But AI is already playing an active role in our day-to-day lives, and its capabilities are only going to increase from here on out. To help ease the anxiety that will likely accompany that increase, Wired founding editor Kevin Kelly has suggested we re-frame the way were thinking about AI, both by changing the vocabulary we use for it and by putting it into historical context.

Kelly thinks the word intelligence has taken on undue baggage, including a somewhat negative connotation. When its not used in reference to a human mind, the word can conjure images of spying, classified information, or invasion of privacy.

Since the scope of artificial intelligence goes far beyond that, and we may be past the point of instilling a new definition of old words, why not use new words instead?

Kellys word of choice is cognification, and he uses it to describe smart things.

At this point only a handful of things have been cognified, and more are in process: phones, cars, thermostats, TVs. But in the future, Kelly says, everything thats already been electrified will also be cognified. Smart homes? Smart office buildings? Smart cities? Only a matter of time.

The cognification of things can be viewed similarly to the electrification of things that took place during the Industrial Revolution.

The industrial revolution saw a large-scale switch from the agricultural worldwhere everything that was made was made by muscle powerto the mechanized world, where gasoline, steam engines, and electricity applied artificial power to everything. We made a grid to deliver that power, so we could have it on-demand anytime and anywhere we wanted, and everything that used to require natural power could be done with artificial power.

Movement and transportation, among other things, were amplified by this new power. Kelly gives the example of a car, which is simple but compelling: you summon the power of 250 horses just by turning a key. Pressing your foot to the gas pedal can make your vehicle go 60 miles an hour, which would have been unthinkable in the era when all we had to go off of was muscle power.

The next step is to take that same car that already has the artificial power of 250 horses and add the power of 250 artificial minds. The result? Self-driving cars that can not only go fast, they can make decisions and judgment calls, deliver us to our destinations, and lower the risk of fatal accidents.

According to Kelly, were currently in the dawn of another industrial revolution . As it progresses, well take everything weve previously electrified, and well cognify it.

Imagining life before the Industrial Revolution, we mostly wonder how we ever lived without electricity and human-made power, thinking, Wow, Im sure glad we have lights and airplanes and email now. Its nice not to have to light candles, ride in covered wagons, or send handwritten letters. Admittedly, our relief is sometimes mixed with some nostalgia for those simpler times.

What will people think in 200 years? Once everything has been cognified and the world is one big smart bubble, people will probably have some nostalgia for the current simpler timesbut theyll also look back and say, How did we ever live without ubiquitous AI?

Image Credit: Shutterstock

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How AI Is Like Electricityand Why That Matters - Singularity Hub

Artificial Intelligence was unable to predict the outcome of the National Collegiate Athletic Association basketball tournament, and entering the AI bracket of Microsoft Corporation (NASDAQ: MSFT)'s Bing into Loup Ventures NCAA bracket contest indicated it was just as busted as the others, Gene Munster said in a report.

Bings bracket is expected to finish in the 7th position, at the bottom of the pool, regardless of the outcome of the game. To date, Bing has correctly predicted the outcome of 47 games out of 69, Munster mentioned.

We would like to think that we outsmarted AI, but the reality is that predicting the outcome of the NCAA tournament is more a matter of luck than skill. Bings performance doesnt mean its broken, just unlucky this year, the analyst wrote.

While some are skeptical of the ability of AI to make predictions, it is a better predictor of outcomes than any human, given the amount of data it can incorporate in its process of prediction.

Although we cannot expect AI to be 100 percent accurate, it will be right more often than humans making the same predictions, Munster noted, while adding, Humans got lucky with our brackets this year, but shouldnt expect an easy repeat next year.

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Patriots Win Caps Most Thrilling Sports Championship Year Ever _________ Image Credit: "Barack Obama fills out 2009 NCAA Men's Div I Tournament bracket 3-17-09" Pete Souza [Public domain], via Wikimedia Commons

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AI's Busted Bracket: What To Expect Next Year - Benzinga

Have you ever thought of working in AI? If so, now is the right time to start

With so much advancement in the AI field, there is no reason to not pursue Artificial Intelligence. On top of that, there is a huge market demand for AI engineers right now.

Multiple online platforms are also making it easy for AI aspirants to showcase their skills and improve themselves. LiveEdu.tv is one of the unique platforms that enable any technology lover to showcase their skills. The platform enables anyone to broadcast their projects. Moreover, the AI aspirants can watch advanced AI projects on the site and have interaction with AI engineers. Udacity is also helping AI aspirants by providing amazing AI related courses and nano degree programs.

So, how come it is the right time to start an AI career? Lets try to answer the question by looking into different aspects of AI.

The first question that always comes up when choosing a career path is the prospects of earning. No one should just pick up a career without knowing how much they can make or will make in future. Of Course, there are always some exceptions when it comes to personal choice where not all weightage is given to payscale. In short, payscale should take an important part of your decision, but should never be of the highest impact.

AI engineers are paid heavily right now, with an average salary of $135K per annum(US), making it one of the best paid jobs right now. Some are already making $250K per annum with options of stock and other benefits.

What if I told you that prominent AI engineers and scientists are already retiring from their work? The reason behind their decision is the amount of money they have already made by working on AI. This single event speaks about the huge market opportunity that new engineers can take right now. All they need to do is start learning AI and pursue it with all the passion and dedication they can ever have.

To become an AI engineer, you dont have to take any special class. All the resources are available online and ready to use. You can follow a simple guide on How to become an Artificial Intelligence engineer to get started. You can also check other resources for starting AI. They are listed below.

Medium Post: List of best resources to learn the foundations of Artificial Intelligence AI Resource page: Offers tons of AI resources. CS Berkeley AI resources Ultimate Artificial Resources guide

Artificial Intelligence growth just didnt happen in one-day. It took decades of work to finally arrive at the point where it is now. The year 2016 can easily be said as the Year of AI. With computer program AlphaGo beating the best Go player, the odds are now in the favor of AI.

The history of Artificial intelligence is rich. It dates back to the Antiquity. However, if we look into the modern AI history, the real deal started in 1956 when John McCarthy coined the word, Artificial Intelligence.

Major improvements are seen in the last three decades. One of the prime examples of AI impact is the self-driving cars.

AI rich history and exponential growth is enough to encourage anyone to start an AI career. Even with huge milestones, many experts believe that AI is still in its infant stage. David Hanson, the founder of the Hanson Robotics refers to AI as smart, but still in the infant stage.

The realization that AI has still a long way to go adds value to anyone who is going to start their AI career.

Artificial Intelligence is currently being used in all the major sectors, including health, social media analysis, self-driving cars, language processing and others. The AlphaGo victory is just one of the signs of amazing things to happen. Many experts believe that AI will continue to grow in the year 2017.

If you see the current advancement, it would be easy to say that the future of AI is promising. However, experts are not sure about how the future of AI will unfold. Sergey Brin, the co-founder of Google is himself speculative on what the future holds for AI.

Until now, we have seen great advancements in AI. There is always an uproar when there is a seismic technological shift which transforms industries. The same is true for the rise of Artificial Intelligence and many people are afraid of losing their job to automation.

Not only white-collar workers, but many IT workers are also threatened with the advancement of AI and its application in automation. Right now, the best way to secure your future is to equip yourself with skills.

Keeping all the above facts in mind, it is the right time to become an Artificial Intelligence Engineer.

So, what do you think about starting an AI career? Is it the right time? Share your views in the comments section below.

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This is the right time to start a career in AI - Geektime - Geektime

Some of the worlds best known brands have invested millions of dollars in information security. So have their adversaries. Malicious actors are counting on the fact that your defenses areoperated mostly by humans who make changes.

When you moved into your neighborhood, did you change your locks or do you have the exact same ones as all your neighbors? Think about what could happen if a thief can compromise just one of those shared locks? For some reason the world of information security has a same-lock mentality. And some of their customers are malicious actors working hard to do harm. Given the situation, we should not be surprised that even with the massive amount of money being spent defenses still fail.

If cyber defenders are ever going to have a chance at winning, we must begin to level this playing field. Vendors distribute identical copies of their security products to customers because its easier for them, not because its better for their customers.

How many variants of a signature is an anti-virus company supposed to produce for each malware sample it analyzes? Do all host-based artificial intelligence (AI) defenses learn in their environment? In the past, tailoring these approaches for each enterprise was not feasible. Luckily, new techniques are emerging within cybersecurity that produce unique detection behaviors for each customer. Behaviors that can help level the playing field, and maybe even help win the game.

These emerging techniques broadly fall into the area of AI and machine learning. At the heart of any AI system is the ability to learn. Some AI solutions learn from their local environment while others learn strictly from a global context. Those solutions that build some or all of their threat detection capability using data that only exists in a customers network environmentand produce a type of moving defense unique to that environment will win out. These include:

Similar tohow adding cryptographyto a password helps protect it from compromise, deploying cybersecurity solutions that use the network environment to differentiate themselves from all other copies helps protect the enterprise from compromise.

AI systems use many thousands of features to discern if content traversing a network is malicious or if user or system behaviors are anomalous. Each feature alone provides only a small piece of evidence needed to make a final determination or classification.

Only in intricate and complex combinations are they useful. Machine learning algorithms try to figure out how to combine features to produce accurate insights and predictions using a dedicated set or period of training.

Depending on each AI systems approach, training data can originatefrom the local environment, a global context or a hybrid of the two. However, unlike traditional approaches, the resulting models are never based on simple rules or patterns easily understood and described by subject matter experts. The natural opacity of these models and their dynamic construction provide the building blocks for an effective moving defense.

You can alter theAI models by adjusting the training set or period. Whether additional training data is simply added or used to replace older training data wont matter the results are the same.

New models are created with different ways of using existing features and possibly using totally new features. With AI and machine learning, the cost of building tailored detection solutions is negligible. There must, however, be a vision on the part of the solution provider to enable this approach. Some security providers using machine learning and AI still deploy their models in a traditional manner and wont leverage the local data for tailoring their solution.

Of course, there are challenges with moving defenses, and not just those faced by the malicious actors that will continue to try to defeat them. The most significant challenge is ensuring parity among the tailored solutions. Nobody wants the second-best detection model. Care must be taken to verify that any technical implementation produces a statistically equivalent model with detection accuracy and error rates nearly identical across all tailored variants.

Its hard to find a security concept simpler than a moving defense. Change your locks is amongst the most well established security advice. In cybersecurity, however, some locks are just easier to change than others.

Scott Miserendino is the chief data scientist at BluVector.

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How AI can 'change the locks' in cybersecurity - VentureBeat

The free, AI-powered app Tambero.com which launched Wednesday will allow some of the worlds poorest farmers to communicate with their cattle using only a smartphone.

It is the next step in technological evolution, the applications founder and creator, Eddie Rodrguez Von Der Becke, said, as cited by Cadena 3.

AI-powered bots assess the animals condition based on a number of inputs, and interact with farmers, reminding them about vaccination and feeding times, and gestation periods, in addition to providing additional tips and information to improve the overall health of the herd.

For now, the system operates via text input alone, but an update due later this year will allow for voice commands, effectively allowing farmers to engage with their herds like never before.

Questions can include: How are you feeling? or are you hungry? and when was your last vaccination? reports El Argentino.

In the initial testing phase of the app, farmers around the world reported up to a three-fold increase in daily milk production.

With the help of Microsoft, we came up with the idea to create one artificial intelligence that analyzes human language and connect it with another that analyzes animal behaviour, he added.

READ MORE: Holy cow! Butchers face life sentence in India for slaughtering sacred animal

Over half the worlds population does not yet have access to the internet, which means connectivity is a global challenge that requires a creative solution, Peggy Johnson, executive vice president of business development at Microsoft, said in a statement.

By using todays technology and working with local business-owners that best understand the needs of their communities, our hope is to create sustainable solutions that will last for years to come, she added.

READ MORE: Court rules over pile of manure so massive it could be seen from space

Tambero.coms main goal is sharing best agricultural practices around the world to improve dairy production and, as a direct consequence, the lives of farmers in some of the poorest countries in the world.

The app works across all platforms and all manner of smartphones, from the earliest generation to the latest, on desktop and laptop to afford users the most flexibility possible.

Cows are better conversationalists than some people I know, Von Der Becke joked on Facebook.

The immediate language barrier issue was overcome in a very straightforward way; by making the language element open source, Tambero.com empowers users to engage with the platform, share not only their languages but also their experiences while also helping to add languages that arent even supported by Google Translate yet, Von Der Becke told a TedX conference in Crdoba, Argentina.

For the first time in history we have the capacity, the knowledge and the tools to resolve some of the most fundamental problems we face as a species: food security, poverty, education, sexual education, sustainable production, Von Der Becke concludes.

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Microsoft AI-powered app lets farmers chat with their cows - RT

US President Donald Trumps reputation as a gonzo salesperson and savvy deal-clincher has been tainted by his inability to pass his health care reform bill, a fumble that may well be the subject of studies and speculation in coming years about what went wrong.

But in the here and now, there are new technologies available to help the US president figure it out. Startups are now developing artificial-intelligence (AI)-based technologies aimed at helping sales teams improve their sills and clinch that slippery deal.

One of them is Chorus.ai, a San Francisco- and Tel Aviv-based startup that uses AI to analyze sales conversations and learn how organizations can increase the number of deals they win.

Our software transcribes what is said in the call and analyzes the conversation: what topics were discussed? what questions were asked? is the person hesitant? Does their tone sound excited or engaged? said Micha Breakstone, co-founder and head of R&D at Chorus.ai in an interview at the companys offices in Tel Aviv. Our aim is to figure out the hidden dimensions that govern outcomes of human conversations.

Chorus.ai Israel-US teams (Courtesy)

The companys technology uses a combination of proprietary speech recognition, natural language processing and AI technologies developed in-house to transcribe, analyze and deliver real-time feedback on sales conversations. The software helps organizations understand their sales calls, detecting the most important moments and learning what teams could do differently to achieve better outcomes. This feedback helps sales managers coach their workers to improve their sales pitches and selling skills, Breakstone said.

In sales every 1 percent improvement in conversation translates to 1% bigger revenues for the company, said Breakstone, who holds a PhD in Cognitive Science from the Hebrew University in Jerusalem and who previously co-founded Gingers Virtual Personal Assistant Platform Business Unit acquired by Intel in 2014.

With sales forces spending thousands or tens of thousands of hours each quarter in online or phone meetings with customers and prospects, conversations are a sales forces most valuable and underutilized asset, he said.

No one has opened up the sales calls for analysis, he said. Conversation Intelligence is such a new concept, it took time to convince people of its value, but now customers are asking for it.

The company, founded two years ago by Roy Raanani, the companys CEO, Breakstone, and Russell Levy as founding CTO, released its software in late 2016 and has since has been selling its product to billion-dollar companies with huge sales forces, according to Breakstone.

Chorus.ai co-founder Roy Raanani, the companys CEO (Courtesy)

Its customers, including Qualtrics, Marketo and Dynamic Signal, have used the Chorus.ai platform during the last year to analyze hundreds of thousands of sales conversations, he said.

This is how it works: Customers use Choruss SaaS software to make calls, prospects are notified the call is being recorded, then Chorus.ais software records, transcribes and analyzes all the conversations in real-time, giving sales managers access to a huge amount of information regarding the sales process that they can then use to coach their reps.

The software also allows for the classification of conversations by topic, time of calls, and speak/listen ratio, and can be set to highlight when specific moments or topics like price or budget are mentioned; and also, what the next steps are, like whether a conversation needs to have a follow-up call or not.

This is coaching on steroids, said Breakstone. Also, we help companies automatically surface what differentiated closing deals vs. failed pitches. We can analyze topics and see if there is a pattern.

The software has found some counterintuitive insights.

More questions are better than fewer, but they need to be open-ended and engaging, Breakstone said. Also, it can sometime be good to mention competitors in the conversation. When a prospect mentions a competitor, they already know the market, and may be closer to making a final decision, he said. And that means the pitch can be changed to home in on a close.

Chorus.ai issues reports that highlight patterns and indicate what could be done better. The company is also working on an update to be released next month that will give sales reps real-time analysis and tips on how they can do better, like, You are talking too fast or too long, or, when such and such question was asked, the best reps have used this answer to close the deal.

Micha Breakstone, Chorus.ai co-founder and president (Courtesy)

Technology is not the hitch to providing immediate feedback, said Breakstone. What makes the matter sensitive is the focus and attention of the salespeople. The big question is how to give them the feedback subtly, without distracting them from the call.

Sales tech startups globally raised $5 billion in funding in 2016, CB Insights, a New York-based data company said in a March 21 report. These include companies that are developing tech-enabled solutions that directly serve sales teams or improve the sales process, as well as serving customer relationship management platforms.

The US Bureau of Labor Statistics estimates that by 2020 there will be 2.6 million inside sales reps, those who work over the phone in the US, up from 1.2 in 2010.

People travel much less these days to sell, said Breakstone. There are fewer face-to-face deals and communications are becoming more virtual. Video conferences are almost as good as fact to face. That is how people are selling today, and we are creating a new product category in this space, he said.

Russell Levy, founding CTO of Chorus.ai (Courtesy)

Chorus.ai keeps the recorded calls as long for as the customer remains on board, then deletes them. The statistical metadata collected, however, stays with Chorus.ai.

At the moment the technology works only with English. But other languages are doable, said Breakstone. We need to invest money and work to do that, he said. It is as if now we have built a stereo system and all we have to do to get it to work with other languages is to change the disk.

Chorus.ai is in a market that is in a positive trajectory point and is expected to grow rapidly in the immediate future, said Zirra.com Ltd., a Tel Aviv-based research firm that analyzes private companies using artificial intelligence and machine learning technologies. However, as there are many existing companies already in the market space, Chorus.ai must display a strong differentiating factor in order to surpass the clutter.

Direct competitors include Deepgram, TalkIQ, and Persado, Zirra said.

In February Chorus.ai raised $16 million in a Series A financing round led by Redpoint Ventures with participation from original seed investor Emergence Capital.

We spent a year researching the market and its clear to us that Chorus.ai is the leader, Tomasz Tunguz, a partner at Redpoint Ventures said at the time of the announcement. Their unique technology enables them to provide real-time feedback to account executives, accelerating training, tuning performance and empowering those teams to win more business every day.

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Donald Trump could sharpen sales skills with AI next time - The Times of Israel

Google invested $5 million CAD in the new Vector Institute in Toronto, Canada, the Mountain View-based tech giant announced. In a blog post published earlier this week,Geoffrey Hinton, Engineering Fellow at Google andChief Scientific Advisor for the Vector Institute, explained that the investment is yet another step in Googles efforts to help grow the artificial intelligence (AI) sector in the country. In addition to the investment meant to kickstart the latest AI institute of the Univesity of Toronto, Hinton also revealed that the Alphabet-owned company just opened another deep learning office in Canada Google Brain Toronto. The new office will be looking to resolve some of the major obstacles that contemporary AI researchers are facing, Hinton said, but didnt provide specific details regarding the offices activities.

Regardless, the Internet giant vowed to continue investing in Canadas growing AI sector by publishing its related findings and assisting researchers and collaborators using TensorFlow, its open source software library for machine learning and artificial intelligence in general. While the companys new Toronto office will apparently be focused on major AI-related challenges, its previously opened research center will continue working on basic advancements in the field, the firm said. Googles $5 million CAD investment is only a smaller portion of the funding secured for the new Vector Institute that already raised $150 million CAD, the majority of which was provided by Canadian and Ontarian administrations. The institutes focus could see it make breakthroughs that will help advance a wide array of industries including manufacturing and healthcare, Hinton said.

Googles new investment comes shortly after the Mountain View-based company gave $4.5 million CAD to another AI institute in the country last November. The companys growing focus on AI has seen it invest in a number of similar initiatives in recent years as Google is currently funding related operations all over the world and is gradually increasing the amount of resources its committing to its AI efforts. While its unlikely that consumers will experience any direct benefits of the Vector Institutes research in the short term, the new Toronto facility is bound to contribute to long-term advancements in this emerging technology on a global scale.

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Google Invests $5 Million In Canadian AI Institute - Android Headlines - Android Headlines

Slide: 1 / of 1. Caption: David Ramos/Getty Images

Americas biggest tech companies are remaking the internet through artificial intelligence. And more than ever, these companies are looking north to Canada for the ideas that will advance AI itself.

This morning, Google announced its starting an AI lab in Toronto. At the same time, its helping to fund a public-private partnership with the University of Toronto to develop and commercialize AI talent and ideas. In November, the company made a similar move in Montreala city that has also attracted Microsofts attention.

The Canadian connection is hardly coincidental: Universities in Toronto and Montreal have played a big role in the rise of deep learning, a collection of AI techniques that allows machines to learn tasks by analyzing large amounts of data. As deep learning remakes the likes of Google and Microsoft, Canada has become a hotbed for new talent.

Geoff Hinton, one of the founding fathers of the deep learning movement and a professor at the University of Toronto, has worked for Google since 2012 and will run its new Toronto lab. Hinton says that this is partly a way for him to spend more of his time in the city. The lab will stay small and focus on basic research, he says. At the same time, it will enable Google to maintain a grip on the AI talent coming out of Torontoa strategic move with deep learning experts among the most prized talent in tech world. There will be new researchers, Hinton says.

Meanwhile, Google is investing $5 million in the Vector Institute, a brand new AI research lab backed by the Ontario government, the Canadian federal government, and as many as thirty other companies. Hinton will serve as a primary advisor. Also based in Toronto, the lab is an effort to bridge the gap between university research and companies like Google. We want to support more research, says Jordan Jacobs, co-founder of an AI company called Layer 6 and a former media and technology lawyer who helped create the new lab. But also help commercializehelp companies that need to hire.

All told, government and corporate players have invested some $180 million in the lab altogether. Its clearly a sign that Canada is serious about cultivating its status as an AI hotbed, even as some in the Trump administration downplay its importance. Weve re-established Torontos preeminence as the center of deep learning, he says (though universities in Britain, France, Switzerland, and other parts of Europe have also played a big part in advancing this movement). Either way Canada is indeed a feast of AI research, and the big American companies want to make sure they have a seat at the table.

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For Google, the AI Talent Race Leads Straight to Canada - WIRED