Last week, Martin Abadi, a computer scientist and member of the Google Brain Team at Alphabet Inc (NASDAQ:GOOG, NASDAQ:GOOGL), along with David Andersen, an associate professor of computer science at Carnegie Mellon University, published a research paper that describes how artificial intelligence entities “can learn how to perform forms of encryption and decryption, and also how to apply these operations selectively in order to meet confidentiality goals.”
In simpler terms, GOOGL created three separate AI entities and instructed two of them to pass secret messages back and forth while the third attempted to intercept and decode them.
When the encryption was broken, subsequent messages were secured with even more advanced methods.
The truly amazing part of the experiment was that “the networks were not taught anything about encryption before the game began, meaning that the strategies they came up with were entirely original.” That also means Alphabet researchers may never know exactly what kind of encryption methods were being used, and they probably won’t be able to crack it, either.
Alphabet and the Competition in the AI Space
The GOOGL Brain Team isn’t the only AI research team competing to develop the next revolutionary advancement in machine learning and automation.
Microsoft Corporation (NASDAQ:MSFT), for example, recently created a brand new division to focus solely on AI, dedicating more than 5,000 employees to expand MSFT’s capabilities and commence in-depth research. International Business Machines Corp. (NYSE:IBM) has long been heralded as a pioneer in the AI space since the now-iconic Watson easily defeated two record-holding “Jeopardy!” contestants in 2011.
Additionally, a host of other massive tech-centric companies have demonstrated interest in furthering artificial intelligence capabilities, including Facebook Inc (NASDAQ:FB), Amazon.com, Inc. (NASDAQ:AMZN), Apple Inc. (NASDAQ:AAPL), Nvidia Corporation (NASDAQ:NVDA) and Tesla Motors Inc (NASDAQ:TSLA).
Not all of these companies have the same goals in mind as Alphabet, but the fact that each of these industry giants has allocated significant financial and human resources to research and development of AI is a crystal clear indication that artificial intelligence will play a huge role in the future of all businesses.
Presently, the only company with an AI focus that’s on a similar plane to the advancements described by The Google Brain Team’s latest research publication — and that could possibly be in a position to challenge GOOGL for dominance — is IBM. Both Alphabet and IBM have, at this stage, adopted a more broad approach to AI development, focusing on improvements in communication, transmission and related logistics.
Competitors, however, each seem to have a rather specific niche to which an AI system or methodology would improve existing processes. TSLA, for example, is focusing the bulk of its AI research on improving its already impressive autopilot technology; FB has been looking to develop better facial recognition capabilities and improve its AI assistant; AMZN continues to work on expanding the functionality of its own popular AI virtual assistant, Alexa.
What Does This Mean for GOOGL Stock?
Other than a demonstration that’s more proof of concept than actual functionality, the AI encryption experiment by itself won’t do much to move the needle for GOOGL stock. However, the success of the AI secret messaging game has far-reaching implications, for both Alphabet and the world, and if Google continues to make new inroads into the AI space, GOOGL stock will definitely realize a benefit.
The power for Alphabet here is its access to near-limitless resources for researchers and developers, plus the fact that advancing technology in general is a solid cornerstone of the entire Google foundation. This is also a significant part of GOOGL stock’s long-lasting strength. Research at Google’s Machine Intelligence division states:
“Research at Google is at the forefront of innovation in Machine Intelligence, with active research exploring virtually all aspects of machine learning, including deep learning and more classical algorithms.”
It’s clear that Alphabet wants to remain a leader in the AI arena, a goal further evidenced by the company’s acquisition of DeepMind in 2014. According to the “About Us” page on its website, “DeepMind is the world leader in artificial intelligence research and its application for positive impact.”
Alphabet Inc (GOOGL) Stock Is Just Getting Started
And, similar to the research paper released last week by members of the Google Brain Team, DeepMind is focused on “developing programs that can learn to solve any complex problem without needing to be taught how.”
As Alphabet continues to expand its efforts to further the AI space, GOOGL stock will benefit in the long run as these advancements are eventually refined for real-world application. Today, the process may seem slow and the results minuscule, but soon it will snowball on itself.
Resources: investorplace.com
Showing posts with label machine learning. Show all posts
Showing posts with label machine learning. Show all posts
Graphcore Secures $30m to Accelerate Artificial Intelligence
Graphcore Ltd, a startup developing new technology to deliver massive acceleration for machine learning and AI applications, has completed a $30m Series-A funding round from a world-class line up of venture capital and strategic investors.
Graphcore founders Nigel Toon, CEO, on left, and Simon Knowles, CTO on right
Graphcore has spent the last two years building an experienced hardware and software team to develop a system designed from the ground up to accelerate both current and next generation machine intelligence applications such as natural language dialogue, autonomous vehicles and personalized medicines.
The company will bring its IPU (Intelligent Processing Unit) system to market in 2017 with the IPU-Appliance™ designed to lower the cost of accelerating AI applications in cloud and enterprise datacenters. The IPU-Appliance aims to increase the performance of both training and inference by between 10x and 100x compared to the fastest systems in use today.
The company also plans to make its low power IPU technology available for embedded consumer applications including autonomous cars, collaborative robots and intelligent mobile devices.
IPU systems will accelerate the full range of training, inference, and prediction approaches. Its huge computational resources and software tools and libraries are flexible and easy to use, allowing researchers to explore machine intelligence across a much broader front than the current focus on feed-forward neural networks. This technology will enable recent success in deep learning to evolve rapidly towards useful, general artificial intelligence.
A Bank of America Merrill Lynch report citing IDC research recently predicted that the AI industry will exceed $70 billion by 2020 and Tractica predicts that spending on hardware for deep learning projects will grow from $436 million in 2015 to $41.5 billion by 2024.
Graphcore CEO and co-founder, Nigel Toon, said, "Machine intelligence will have a bigger impact on our lives over the next 10 years than mobile technology has had in the last two decades. Next generation machine intelligence will allow us to translate foreign languages in real-time, help diagnose illnesses and develop personalized treatments, control robots that clean our houses and offices, drive cars autonomously and provide us with intelligent digital assistants that can help us organize our busy lives. The IPU is the first system specifically designed for machine intelligence."
Graphcore CTO and co-founder, Simon Knowles, said, "For 70 years we have built computers to do exactly what a software program says, step by step. The program is an algorithm for solving a problem, and that algorithm must come from a human. Today's computers do not actually help the human to solve the problem. Machine intelligence is turning that on its head. Intelligent machines can analyse data like humans, discover underlying patterns, and effectively write their own programs. They can then adapt their behaviour through trial and error, like humans. They can deal with probabilities and exercise judgement in the presence of uncertainty, like humans."
"We are at the dawn of this second age of computing, in which machines are given the capacity for intelligence. The value to society of intelligent computing will be far greater than that of all computing so far. Silicon is still our best technology for building such machines, but the design details will be quite different from today's microprocessors. Graphcore is at the vanguard of this revolution in computer design and has assembled a peerless engineering team to deliver the first processors designed from scratch for general intelligence."
Linley Gwennap principal analyst of The Linley Group commented, "Machine intelligence and deep learning applications are now popular enough to justify new silicon approaches. The team at Graphcore has a strong track record of creating successful new processors for emerging markets."
Dr Hongquan Jiang, Partner at Robert Bosch Venture Capital GmbH added, "Graphcore has a unique technology that has massive potential in the fast emerging market for deep learning. A new processor technology is needed for intelligent systems and Graphcore has the first technology that we have seen which really delivers the performance and efficiency needed for this style of compute. We are excited to have led this very significant investment round."
"Graphcore's approach is unique in its capability to enable advanced intelligent systems," said Ekaterina Almasque, Managing Director, Samsung Catalyst Fund, Europe. "It closes the gap between the level of intelligence we want to see on edge devices and compute limitations of existing hardware architectures. At Samsung Catalyst Fund, we invest in disruptive companies in this space, and believe tremendous value will be created in the next 10 years by artificial intelligence applications. Graphcore will play an important role as a key enabling technology."
Hermann Hauser, co-founder at Amadeus Capital Partners and a renowned technology entrepreneur, said, "I have worked with Nigel and Simon before in their previous companies where they achieved over $1bn in successful exits for their investors. The team they have assembled is second to none. Machine learning is becoming a major market and Graphcore has the technology to lead this next wave of computing."
Bill Elmore, General Partner and co-founder at Foundation Capital, one of Silicon-Valley's top venture capital firms behind a large number of highly successful companies including Netflix, said, "Graphcore will lower the cost of accelerating AI applications in the cloud. This is exactly the type of world-class systems company, with breakthrough technology, and a great team, that Foundation Capital supports."
Simon Cook, CEO Draper Esprit Plc, stated, "Graphcore is one of the first investments made since we publicly listed in London and Dublin. This is a sector we know well and a founding team that we have successfully backed before. Nigel and Simon have created an exciting company developing next generation processor technology and we are pleased to be investing at this key stage in its development."
Pascal Cagni, Founding Partner of C4 Ventures, Apple GM and VP EMEA (2000-2012), said: "Since its foundation C4 Ventures has been backing hardware companies revolutionising their sector and we believe Graphcore's disruptive technology is a game changer in the computing field. Graphcore's solution pushes further the boundaries of Machine Intelligence, which will unlock value across every industry."
Eyal Niv, Managing General Partner at Pitango Venture Capital, said: "We are very excited to become part of Graphcore and to be backing great entrepreneur like Nigel and Simon. I believe we are on the verge of a new and smarter era, in which computer intelligence, machine learning and deep learning, will transform every aspect of our lives. Smart personalized medicine, autonomous transportation and robotics, smart infrastructure and accurate business prediction are just some of the areas which will be transformed and immensely improved by machine learning technologies. I really believe machine learning will bring about the biggest transformation ever, bigger than the internet, mobile and social put together."
Resources: prnewswire.com
Graphcore founders Nigel Toon, CEO, on left, and Simon Knowles, CTO on right
Graphcore has spent the last two years building an experienced hardware and software team to develop a system designed from the ground up to accelerate both current and next generation machine intelligence applications such as natural language dialogue, autonomous vehicles and personalized medicines.
The company will bring its IPU (Intelligent Processing Unit) system to market in 2017 with the IPU-Appliance™ designed to lower the cost of accelerating AI applications in cloud and enterprise datacenters. The IPU-Appliance aims to increase the performance of both training and inference by between 10x and 100x compared to the fastest systems in use today.
The company also plans to make its low power IPU technology available for embedded consumer applications including autonomous cars, collaborative robots and intelligent mobile devices.
IPU systems will accelerate the full range of training, inference, and prediction approaches. Its huge computational resources and software tools and libraries are flexible and easy to use, allowing researchers to explore machine intelligence across a much broader front than the current focus on feed-forward neural networks. This technology will enable recent success in deep learning to evolve rapidly towards useful, general artificial intelligence.
A Bank of America Merrill Lynch report citing IDC research recently predicted that the AI industry will exceed $70 billion by 2020 and Tractica predicts that spending on hardware for deep learning projects will grow from $436 million in 2015 to $41.5 billion by 2024.
Graphcore CEO and co-founder, Nigel Toon, said, "Machine intelligence will have a bigger impact on our lives over the next 10 years than mobile technology has had in the last two decades. Next generation machine intelligence will allow us to translate foreign languages in real-time, help diagnose illnesses and develop personalized treatments, control robots that clean our houses and offices, drive cars autonomously and provide us with intelligent digital assistants that can help us organize our busy lives. The IPU is the first system specifically designed for machine intelligence."
Graphcore CTO and co-founder, Simon Knowles, said, "For 70 years we have built computers to do exactly what a software program says, step by step. The program is an algorithm for solving a problem, and that algorithm must come from a human. Today's computers do not actually help the human to solve the problem. Machine intelligence is turning that on its head. Intelligent machines can analyse data like humans, discover underlying patterns, and effectively write their own programs. They can then adapt their behaviour through trial and error, like humans. They can deal with probabilities and exercise judgement in the presence of uncertainty, like humans."
"We are at the dawn of this second age of computing, in which machines are given the capacity for intelligence. The value to society of intelligent computing will be far greater than that of all computing so far. Silicon is still our best technology for building such machines, but the design details will be quite different from today's microprocessors. Graphcore is at the vanguard of this revolution in computer design and has assembled a peerless engineering team to deliver the first processors designed from scratch for general intelligence."
Linley Gwennap principal analyst of The Linley Group commented, "Machine intelligence and deep learning applications are now popular enough to justify new silicon approaches. The team at Graphcore has a strong track record of creating successful new processors for emerging markets."
Dr Hongquan Jiang, Partner at Robert Bosch Venture Capital GmbH added, "Graphcore has a unique technology that has massive potential in the fast emerging market for deep learning. A new processor technology is needed for intelligent systems and Graphcore has the first technology that we have seen which really delivers the performance and efficiency needed for this style of compute. We are excited to have led this very significant investment round."
"Graphcore's approach is unique in its capability to enable advanced intelligent systems," said Ekaterina Almasque, Managing Director, Samsung Catalyst Fund, Europe. "It closes the gap between the level of intelligence we want to see on edge devices and compute limitations of existing hardware architectures. At Samsung Catalyst Fund, we invest in disruptive companies in this space, and believe tremendous value will be created in the next 10 years by artificial intelligence applications. Graphcore will play an important role as a key enabling technology."
Hermann Hauser, co-founder at Amadeus Capital Partners and a renowned technology entrepreneur, said, "I have worked with Nigel and Simon before in their previous companies where they achieved over $1bn in successful exits for their investors. The team they have assembled is second to none. Machine learning is becoming a major market and Graphcore has the technology to lead this next wave of computing."
Bill Elmore, General Partner and co-founder at Foundation Capital, one of Silicon-Valley's top venture capital firms behind a large number of highly successful companies including Netflix, said, "Graphcore will lower the cost of accelerating AI applications in the cloud. This is exactly the type of world-class systems company, with breakthrough technology, and a great team, that Foundation Capital supports."
Simon Cook, CEO Draper Esprit Plc, stated, "Graphcore is one of the first investments made since we publicly listed in London and Dublin. This is a sector we know well and a founding team that we have successfully backed before. Nigel and Simon have created an exciting company developing next generation processor technology and we are pleased to be investing at this key stage in its development."
Pascal Cagni, Founding Partner of C4 Ventures, Apple GM and VP EMEA (2000-2012), said: "Since its foundation C4 Ventures has been backing hardware companies revolutionising their sector and we believe Graphcore's disruptive technology is a game changer in the computing field. Graphcore's solution pushes further the boundaries of Machine Intelligence, which will unlock value across every industry."
Eyal Niv, Managing General Partner at Pitango Venture Capital, said: "We are very excited to become part of Graphcore and to be backing great entrepreneur like Nigel and Simon. I believe we are on the verge of a new and smarter era, in which computer intelligence, machine learning and deep learning, will transform every aspect of our lives. Smart personalized medicine, autonomous transportation and robotics, smart infrastructure and accurate business prediction are just some of the areas which will be transformed and immensely improved by machine learning technologies. I really believe machine learning will bring about the biggest transformation ever, bigger than the internet, mobile and social put together."
Resources: prnewswire.com
Artificial Intelligence and the Evolution of a Smarter Internet
‘Let’s build a single supercomputer that is smarter than all of humanity put together.’ While that sounds ominously similar to what a mad scientist in a fictional universe would say before unleashing a deadly robotic that destroys all humanity, that is exactly the ideal behind the advancement of artificial intelligence as we have come to call it. Technologists around the world have long fantasized about an artificial intelligence so powerful, that it is smarter than all of humanity combined.They have had romantic dreams about how an advancement of that caliber could forward the scientific advancement of the human race by several millennia, provided it doesn’t kill them first. While such a negative utopia is still far from being realized, the development of artificial intelligence has officially commenced, the only question that remains is whether it will be humanity’s greatest achievement or its biggest mistake.
Artificial intelligence is a vast and expansive genre. Progress in this genre will obviously lead to massive changes in almost every faction of life, depending on how technology as we know it impacts it today. Most of the AI manufactured today can expertize in no more than one area of intelligence and nothing more. One such example is the AI chess bot that can beat any known human in its own game, but that’s basically all it can do. The artificial intelligence in these devices is limited to a single area of functioning. While this means that a multipurpose domestic robot with near-human intelligence is yet to be scientifically possible, the drawback also allows inventors to focus on the development of intelligence in a specific genre of impact and make significant changes to it. And as long as we are talking about AI and its impact on specific areas of human life, why not discuss when and how artificial intelligence can impact and transform the internet as we know it.
For a while, the concept of AI for the Internet seemed to be pretty much in the hands of giant powerhouses like Google and Facebook, each doing its best to monopolize the genre for profit. But lately, smaller organizations with none of the funds have come up with revolutionary ideas on how to improve the web using artificial intelligence. Artificial Intelligence has already been creatively implemented in several ways across the internet to highlight content based on user-preference, show targeted advertisements, predict and manipulate behavioral traits amongst users and even create and design high quality content in a breeze.
One of the most popular ways artificial intelligence has found use on the internet is via its ability to intelligently target visitors based on their behavioral patterns and use the data thus collected to supply them with content recommendations. Cybernetic giants like Google and Facebook have been known to adapt this technology quite welcomingly. Rankbrain, the revolutionary new algorithm from Google, makes use of artificial intelligence to process unique search engine queries and supply users with customized results. AdWords, Google’s advertisement counterpart, makes heavy use of artificial intelligence to target visitors on the web and supply them with tethered advertisements customized according to their behavioral patterns. As for Facebook, Mark Zuckerberg has made quite a sensation these days after announcing that Facebook will be using AI to sort items in its news feed. Apart from these, several content developers such as Netflix and Amazon Cloud have adapted similar artificial intelligence technologies to target users and provide them with a selective assortment of relevant content based on their browsing history.
Given the enormous success of artificial intelligence in the aforementioned field, it is only natural that a lot of businesses from the eCommerce industry have decided to invest their time and money into using AI to transform and improve product sales. By minutely observing the online behavior of potential buyers, AI can allow any eCommerce to display targeted product recommendations and thus improve their chance of sales from new and returning buyers. This way, artificial intelligence can automate the process of eCommerce, making it cheaper and more convenient, while in no way compromising the amount or quality of conversions generated.
Of course, analytics isn’t the only field of the internet that AI has found use in. Lately, some promising startup organizations have come up with rather innovative ways of making use of artificial intelligence to make the internet a better place through the creation of quality content. A rather prominent one would be Grid, the website building software that was the very first to make use of artificial intelligence to automate the process of web designing and deliver a stunning website in a matter of minutes. Another technology that is worth mentioning would be Wordsmith, the AI-based ‘natural language generation platform’ that can turn raw data into insightful news clips that make online journalism a breeze. There is also a host of AI-based applications that can simplify the process of maintaining a social presence by automating your posts on social media platforms like Facebook and Twitter, though nothing significant comes to mind as most of it is still in beta.
But that’s just the internet of humans. What about the Internet of Things? The internet of things is an automated network that allows for the free exchange of information and data amongst physical objects under the control of an external system. This allows the various devices integrated into a network to remain on the same page and function hand-in-hand in a coordinated system. However, given that the amount of data generated via the IoT is enormous, the devices within the network must rely on some form of artificial intelligence in order to be able to process said data and use it productively. This is done via a segment of artificial intelligence known as machine learning, which forms the basis of the internet of things and other related concepts. The internet of things is primarily dependent on artificial intelligence to say the least, as it allows the network to read and understand the enormous amounts of data without needing the presence of a human operator.
In 2016, the internet is the quite clearly the fastest growing industry in the world. Apart from being a storehouse of information and data, it offers us a thousand ways to educate, entertain and employ ourselves every single day of our lives. If we want to use AI to transform the way the world works, there is no better place to start with than the internet itself. If artificial intelligence can truly transform the way we create, send and receive information online, it has the potential to change the entire world.
Resources: huffingtonpost.com
Artificial intelligence is a vast and expansive genre. Progress in this genre will obviously lead to massive changes in almost every faction of life, depending on how technology as we know it impacts it today. Most of the AI manufactured today can expertize in no more than one area of intelligence and nothing more. One such example is the AI chess bot that can beat any known human in its own game, but that’s basically all it can do. The artificial intelligence in these devices is limited to a single area of functioning. While this means that a multipurpose domestic robot with near-human intelligence is yet to be scientifically possible, the drawback also allows inventors to focus on the development of intelligence in a specific genre of impact and make significant changes to it. And as long as we are talking about AI and its impact on specific areas of human life, why not discuss when and how artificial intelligence can impact and transform the internet as we know it.
For a while, the concept of AI for the Internet seemed to be pretty much in the hands of giant powerhouses like Google and Facebook, each doing its best to monopolize the genre for profit. But lately, smaller organizations with none of the funds have come up with revolutionary ideas on how to improve the web using artificial intelligence. Artificial Intelligence has already been creatively implemented in several ways across the internet to highlight content based on user-preference, show targeted advertisements, predict and manipulate behavioral traits amongst users and even create and design high quality content in a breeze.
One of the most popular ways artificial intelligence has found use on the internet is via its ability to intelligently target visitors based on their behavioral patterns and use the data thus collected to supply them with content recommendations. Cybernetic giants like Google and Facebook have been known to adapt this technology quite welcomingly. Rankbrain, the revolutionary new algorithm from Google, makes use of artificial intelligence to process unique search engine queries and supply users with customized results. AdWords, Google’s advertisement counterpart, makes heavy use of artificial intelligence to target visitors on the web and supply them with tethered advertisements customized according to their behavioral patterns. As for Facebook, Mark Zuckerberg has made quite a sensation these days after announcing that Facebook will be using AI to sort items in its news feed. Apart from these, several content developers such as Netflix and Amazon Cloud have adapted similar artificial intelligence technologies to target users and provide them with a selective assortment of relevant content based on their browsing history.
Given the enormous success of artificial intelligence in the aforementioned field, it is only natural that a lot of businesses from the eCommerce industry have decided to invest their time and money into using AI to transform and improve product sales. By minutely observing the online behavior of potential buyers, AI can allow any eCommerce to display targeted product recommendations and thus improve their chance of sales from new and returning buyers. This way, artificial intelligence can automate the process of eCommerce, making it cheaper and more convenient, while in no way compromising the amount or quality of conversions generated.
Of course, analytics isn’t the only field of the internet that AI has found use in. Lately, some promising startup organizations have come up with rather innovative ways of making use of artificial intelligence to make the internet a better place through the creation of quality content. A rather prominent one would be Grid, the website building software that was the very first to make use of artificial intelligence to automate the process of web designing and deliver a stunning website in a matter of minutes. Another technology that is worth mentioning would be Wordsmith, the AI-based ‘natural language generation platform’ that can turn raw data into insightful news clips that make online journalism a breeze. There is also a host of AI-based applications that can simplify the process of maintaining a social presence by automating your posts on social media platforms like Facebook and Twitter, though nothing significant comes to mind as most of it is still in beta.
But that’s just the internet of humans. What about the Internet of Things? The internet of things is an automated network that allows for the free exchange of information and data amongst physical objects under the control of an external system. This allows the various devices integrated into a network to remain on the same page and function hand-in-hand in a coordinated system. However, given that the amount of data generated via the IoT is enormous, the devices within the network must rely on some form of artificial intelligence in order to be able to process said data and use it productively. This is done via a segment of artificial intelligence known as machine learning, which forms the basis of the internet of things and other related concepts. The internet of things is primarily dependent on artificial intelligence to say the least, as it allows the network to read and understand the enormous amounts of data without needing the presence of a human operator.
In 2016, the internet is the quite clearly the fastest growing industry in the world. Apart from being a storehouse of information and data, it offers us a thousand ways to educate, entertain and employ ourselves every single day of our lives. If we want to use AI to transform the way the world works, there is no better place to start with than the internet itself. If artificial intelligence can truly transform the way we create, send and receive information online, it has the potential to change the entire world.
Resources: huffingtonpost.com
If you think AI isn't a threat to your job, you’re wrong
Some 80% of Americans expect that their jobs will remain untouched by artificial intelligence. A panel at Milken Global of leading AI experts proved that some 80% of Americans are wrong.
It is very early days in the brave new world of artificial intelligence. We have companies like Facebook, Microsoft, Google and more getting a lot of press for early victories with chatbots, driverless cars and other AI-related tech. But to think that AI is only a reality within the tech industry would be a mistake.
To start the panel discussion entitled "Artificial Intelligence: Friend or Foe?" Bloomberg Beta Partner Shivon Zilis showed a slide to demonstrate just how vast the reach of AI already is:
If the slide is overwhelming -- that's by design. What you are looking at is a list of startups that have some sort of machine learning capacity that are impacting industries as diverse as education to agriculture and enterprises as distinct as sales and security. As an investor interested in companies that are impacting the future of work, Zilis keeps an up to date map of all the startups impacting the AI space. The list grows every day and the problems the startups are tackling with machine learning only get bigger, she said.
"We are in the very first inning of a nine-inning game, and there is no industry that is untouched by machine intelligence today," she said.
The obvious dooms day conclusion to jump to after digesting Zilis' slide is that it's only a matter of time before a large portion of our labor market is unemployed. If artificial intelligence can automate jobs as diverse as farming fields to filtering spam out of websites, there is arguably no portion of the workforce that won't be touched in some way by these technological advances.
David Siegel, the co-chairman of tech-assisted hedge fund Two Sigma, says this reality should be top of mind for anyone who touches the labor market. Another panelist argued that this technological upheaval is not unlike the Industrial Revolution. Yet Siegel warned that in fact it is very different because while the Industrial Revolution gave way to millions of new jobs, it is unclear what jobs will be created when machines begin to takeover jobs in transportation, mining and agriculture.
"Vast numbers of jobs that us humans are doing will be done by algorithms," he said. "One of the big causes for the stagnation of middle class wages is because of clever computer programs."
To focus the machine takeover on blue collar work is missing the larger problem, said Merrick Ventures founder Michael Ferro. The trend he is tracking is how the AI-revolution will impact white collar jobs and the distribution of wealth in the country. Ferro says he knows hundreds of entrepreneurs who are working on everything from sensors on cars to algorithms for training that are going to make them a ton of money.
"Nobody is susceptible to how AI will shift a thousand billionaires to a group of another thousand billionaires," he said. "If your companies haven't adopted some [AI] strategy, there is some 18 year-old or 28 year-old or 88 year-old who is going to be adopting this technology. It will be a seismic shift."
So how do we guarantee that the machines don't take over? Stuart Russell, a professor at University of California, Berkeley and vice chair of the World Economic Forum Council on AI and Robotics, put things in perspective by bringing it back to our humanity and happiness. While a majority of the people in the audience at Milken likely enjoy their jobs, that is not a reality for millions of workers around the world, he said. If we can automatize the tedious, routine and unfulfilling tasks to machines to make way for workers to do more advanced work, that's ultimately a trend that leaders should get behind.
What remains to be seen is how exactly the labor market will shift to create new jobs to replace the ones we're giving to machines, warned Siegel of Two Sigma again.
"Will automation spur growth? We should be optimistic that we will come up with all these high-value things for our labor force to do, but I don't think we can assume it will happen," he said.
Resources: linkedin.com
It is very early days in the brave new world of artificial intelligence. We have companies like Facebook, Microsoft, Google and more getting a lot of press for early victories with chatbots, driverless cars and other AI-related tech. But to think that AI is only a reality within the tech industry would be a mistake.
To start the panel discussion entitled "Artificial Intelligence: Friend or Foe?" Bloomberg Beta Partner Shivon Zilis showed a slide to demonstrate just how vast the reach of AI already is:
If the slide is overwhelming -- that's by design. What you are looking at is a list of startups that have some sort of machine learning capacity that are impacting industries as diverse as education to agriculture and enterprises as distinct as sales and security. As an investor interested in companies that are impacting the future of work, Zilis keeps an up to date map of all the startups impacting the AI space. The list grows every day and the problems the startups are tackling with machine learning only get bigger, she said.
"We are in the very first inning of a nine-inning game, and there is no industry that is untouched by machine intelligence today," she said.
The obvious dooms day conclusion to jump to after digesting Zilis' slide is that it's only a matter of time before a large portion of our labor market is unemployed. If artificial intelligence can automate jobs as diverse as farming fields to filtering spam out of websites, there is arguably no portion of the workforce that won't be touched in some way by these technological advances.
David Siegel, the co-chairman of tech-assisted hedge fund Two Sigma, says this reality should be top of mind for anyone who touches the labor market. Another panelist argued that this technological upheaval is not unlike the Industrial Revolution. Yet Siegel warned that in fact it is very different because while the Industrial Revolution gave way to millions of new jobs, it is unclear what jobs will be created when machines begin to takeover jobs in transportation, mining and agriculture.
"Vast numbers of jobs that us humans are doing will be done by algorithms," he said. "One of the big causes for the stagnation of middle class wages is because of clever computer programs."
To focus the machine takeover on blue collar work is missing the larger problem, said Merrick Ventures founder Michael Ferro. The trend he is tracking is how the AI-revolution will impact white collar jobs and the distribution of wealth in the country. Ferro says he knows hundreds of entrepreneurs who are working on everything from sensors on cars to algorithms for training that are going to make them a ton of money.
"Nobody is susceptible to how AI will shift a thousand billionaires to a group of another thousand billionaires," he said. "If your companies haven't adopted some [AI] strategy, there is some 18 year-old or 28 year-old or 88 year-old who is going to be adopting this technology. It will be a seismic shift."
So how do we guarantee that the machines don't take over? Stuart Russell, a professor at University of California, Berkeley and vice chair of the World Economic Forum Council on AI and Robotics, put things in perspective by bringing it back to our humanity and happiness. While a majority of the people in the audience at Milken likely enjoy their jobs, that is not a reality for millions of workers around the world, he said. If we can automatize the tedious, routine and unfulfilling tasks to machines to make way for workers to do more advanced work, that's ultimately a trend that leaders should get behind.
What remains to be seen is how exactly the labor market will shift to create new jobs to replace the ones we're giving to machines, warned Siegel of Two Sigma again.
"Will automation spur growth? We should be optimistic that we will come up with all these high-value things for our labor force to do, but I don't think we can assume it will happen," he said.
Resources: linkedin.com
How Economists View the Rise of Artificial Intelligence
Machine learning will drop the cost of making predictions, but raise the value of human judgement.
To really understand the impact of artificial intelligence in the modern world, it’s best to think beyond the mega-research projects like those that helped Google recognize cats in photos.
According to professor Ajay Agrawal of the University of Toronto, humanity should be pondering how the ability of cutting edge A.I. techniques like deep learning—which has boosted the ability for computers to recognize patterns in enormous loads of data—could reshape the global economy.
However, one group of people refused to call the Internet a new economy: economists. For them, the Internet didn’t usher in a new economy per se, instead it simply altered the existing economy by introducing a new way to purchase goods like shoes or toothbrushes at a cheaper rate than brick-and-mortar stores offered.
“Economists think of technology as drops in the cost of particular things,” Agrawal said.
Likewise, the advent of calculators or rudimentary computers lowered the cost for people to perform basic arithmetic, which aided workers at the census bureau who previously slaved away for hours manually crunching data without the help of those tools.
Similarly, with the rise of digital cameras, improvements in software and hardware helped manufacturers run better internal calculations within the device that could help users capture and improve their digital photos. Researchers essentially applied calculations to the old-school field of photography, something previous generations probably never believed would be touched by math, he explained.
As people “we shifted to an arithmetic solution” to help improve digital cameras, but their cost went up as more people wanted them, as opposed to traditional film cameras that require film and chemical baths to produce good photos, he added. “Those went down,” said Agrawal, in terms of both cost and want.
All this takes us back to the rise of machine learning and its ability to learn from data and make predictions based on the information.
The rise of machine learning will lead to “a drop in the cost of prediction,” he said. However, this drop will result in certain other things to go up in value, he explained.
For example, a doctor that works on a patient with a hurt leg will probably have to take an x-ray of the limb and ask questions to gather information so that he or she can make a prediction on what to do next. Advanced data analytics, however, would presumably make it easier to predict the best course of remedy for the doctor, but it will be up for the doctor to follow through or not.
So while “machine intelligence is a substitute for human prediction,” it can also be “a compliment to human judgment, so the value of human judgment increases,” Agrawal said.
In some ways, Agrawal’s comments call to mind a recent research paper in which researchers developed an A.I. system that could predict 79% of the time the correct outcome of roughly 600 human rights cases by the European Court of Human Rights. The report’s authors explained that while the tool could help discover patterns in the court cases, “they do not believe AI will be able to replace human judgement,” as reported by the Verge.
The authors of that research paper don’t want A.I. powered computers to replace humans as new, futuristic cyber judges. Instead, they want the tool to help humans to make more thoughtful judgements that can ultimately improve human rights.
References: fortune.com
To really understand the impact of artificial intelligence in the modern world, it’s best to think beyond the mega-research projects like those that helped Google recognize cats in photos.
According to professor Ajay Agrawal of the University of Toronto, humanity should be pondering how the ability of cutting edge A.I. techniques like deep learning—which has boosted the ability for computers to recognize patterns in enormous loads of data—could reshape the global economy.
However, one group of people refused to call the Internet a new economy: economists. For them, the Internet didn’t usher in a new economy per se, instead it simply altered the existing economy by introducing a new way to purchase goods like shoes or toothbrushes at a cheaper rate than brick-and-mortar stores offered.
“Economists think of technology as drops in the cost of particular things,” Agrawal said.
Likewise, the advent of calculators or rudimentary computers lowered the cost for people to perform basic arithmetic, which aided workers at the census bureau who previously slaved away for hours manually crunching data without the help of those tools.
Similarly, with the rise of digital cameras, improvements in software and hardware helped manufacturers run better internal calculations within the device that could help users capture and improve their digital photos. Researchers essentially applied calculations to the old-school field of photography, something previous generations probably never believed would be touched by math, he explained.
As people “we shifted to an arithmetic solution” to help improve digital cameras, but their cost went up as more people wanted them, as opposed to traditional film cameras that require film and chemical baths to produce good photos, he added. “Those went down,” said Agrawal, in terms of both cost and want.
All this takes us back to the rise of machine learning and its ability to learn from data and make predictions based on the information.
The rise of machine learning will lead to “a drop in the cost of prediction,” he said. However, this drop will result in certain other things to go up in value, he explained.
For example, a doctor that works on a patient with a hurt leg will probably have to take an x-ray of the limb and ask questions to gather information so that he or she can make a prediction on what to do next. Advanced data analytics, however, would presumably make it easier to predict the best course of remedy for the doctor, but it will be up for the doctor to follow through or not.
So while “machine intelligence is a substitute for human prediction,” it can also be “a compliment to human judgment, so the value of human judgment increases,” Agrawal said.
In some ways, Agrawal’s comments call to mind a recent research paper in which researchers developed an A.I. system that could predict 79% of the time the correct outcome of roughly 600 human rights cases by the European Court of Human Rights. The report’s authors explained that while the tool could help discover patterns in the court cases, “they do not believe AI will be able to replace human judgement,” as reported by the Verge.
The authors of that research paper don’t want A.I. powered computers to replace humans as new, futuristic cyber judges. Instead, they want the tool to help humans to make more thoughtful judgements that can ultimately improve human rights.
References: fortune.com
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