‘We’re on the right ladder of AI this time’: Satya Nadella – Mashable


The Hindu
'We're on the right ladder of AI this time': Satya Nadella
Mashable
Microsoft CEO Satya Nadella spoke at a public event in India on Monday, stressing upon the immense potential of artificial intelligence (AI), calling it the "ultimate breakthrough" in technology. SEE ALSO: Here's why those tech billionaires are ...
Nadella woos India Inc. with artificial intelligenceThe Hindu
Without reskilling, artificial intelligence will impact jobs, warns NadellaBusiness Standard
Flipkart, Microsoft partner up; Flipkart incorporates Microsoft Azure to improve the e-commerce experience for IndiansIndia.com
Times of India -Financial Express -Microsoft News Center
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"brain scans" of artificial intelligence processes – Boing Boing

Graphcore produced a series of striking images of computational graphs mapped to its "Intelligent Processing Unit."

The graph compiler builds up an intermediate representation of the computational graph to be scheduled and deployed across one or many IPU devices. The compiler can display this computational graph, so an application written at the level of a machine learning framework reveals an image of the computational graph which runs on the IPU.

The image below shows the graph for the full forward and backward training loop of AlexNet, generated from a TensorFlow description.

Our Poplar graph compiler has converted a description of the network into a computational graph of 18.7 million vertices and 115.8 million edges. This graph represents AlexNet as a highly-parallel execution plan for the IPU. The vertices of the graph represent computation processes and the edges represent communication between processes. The layers in the graph are labelled with the corresponding layers from the high level description of the network. The clearly visible clustering is the result of intensive communication between processes in each layer of the network, with lighter communication between layers.

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Top 3 Trends Impacting the Artificial Intelligence Market in the US Education Sector Through 2021: Technavio – Yahoo Finance

LONDON--(BUSINESS WIRE)--

Technavios latest market research report on the artificial intelligence market in the US education sector provides an analysis of the most important trends expected to impact the market outlook from 2017-2021. Technavio defines an emerging trend as a factor that has the potential to significantly impact the market and contribute to its growth or decline.

This Smart News Release features multimedia. View the full release here: http://www.businesswire.com/news/home/20170220005208/en/

Jhansi Mary, a lead analyst from Technavio, specializing in research on education technology sector says, The artificial intelligence market in the US education sector is expected to grow at a spectacular CAGR of more than 47%. The US education system is the pioneer in implementing education technology solutions with the objective to improve the quality of education imparted to students and consequently the graduation rates. Therefore, many public and private educational institutions in the US are investing large resources in implementing the digitization of education.

Request a sample report: http://www.technavio.com/request-a-sample?report=56665

Technavios sample reports are free of charge and contain multiple sections of the report including the market size and forecast, drivers, challenges, trends, and more.

The top three emerging market trends driving the artificial intelligence market in the US education sector according to Technavio education research analysts are:

Artificial intelligence-empowered educational games

Educational games provide teachers a useful medium to teach education concepts in an interactive and engaging manner. Such a method not only generates curiosity but also motivates through reward points, badges, and levels. Vendors are incorporating features of artificial intelligence in games to enhance the interactivity element. These games embody the adaptive learning feature so that students can be given frequent and timely suggestions for a guided learning experience. These games improvise adaptive learning features while deploying machine learning, a form of artificial intelligence.

Duolingo, an open-source provider of language learning courses and games, received an investment of USD 45 million from Google Capital in 2015. This investment was used to bolster the deployment of machine learning and NLP technologies in their products.

Assists collaborative learning model

With new learning models, such as blended learning and flipped classrooms, evolving there is a growing focus by educators to create an environment that facilitates collaborative learning among students. Teachers have been introducing various group activities, such as role plays and problem-solving exercises, to motivate students to learn together.

Artificial intelligence will yield a more scientific outlook toward developing collaboration with practical methods such as virtual agents, adaptive group formation, intelligent facilitation, and expert moderation. Such defined models will ease the implementation and assessment of collaborative learning models.

All these methods have been designed to foster efficient group activities by considering each student's learning needs and cognitive skills. While performing activities, students are provided with appropriate guidance, advice, and inputs by the artificial intelligent system. This ensures teachers obtain the desired outcomes, says Jhansi.

Facilitates better course designing activities

Content analytics consist of techniques that assist course content designers in designing, developing, modifying, and upgrading content based on the needs of learners. Owing to the benefits it provides it has increasingly become a part of the technology-enabled education system. Artificial intelligence has taken this technique a step forward ever since its introduction.

Companies, such as IBM, are combining cognitive neuroscience and cognitive computing to harness the benefits of artificial intelligence in educational content. It uses research in the field of cognitive neuroscience to understand human learning and other cognitive processes. These inputs help to create content that will be more engaging and interactive for learners.

Coursera, a massive online open course (MOOC) provider, offers content backed by artificial intelligence software. It helps in closing loopholes present in the education content, such as differences in lecture notes and educational materials. Therefore, the presence of such smart software will positively impact the market growth.

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About Technavio

Technavio is a leading global technology research and advisory company. The company develops over 2000 pieces of research every year, covering more than 500 technologies across 80 countries. Technavio has about 300 analysts globally who specialize in customized consulting and business research assignments across the latest leading edge technologies.

Technavio analysts employ primary as well as secondary research techniques to ascertain the size and vendor landscape in a range of markets. Analysts obtain information using a combination of bottom-up and top-down approaches, besides using in-house market modeling tools and proprietary databases. They corroborate this data with the data obtained from various market participants and stakeholders across the value chain, including vendors, service providers, distributors, re-sellers, and end-users.

If you are interested in more information, please contact our media team at media@technavio.com.

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This New Supercomputer Will Help Meet Demands Of Artificial Intelligence And Big Data – Forbes


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This New Supercomputer Will Help Meet Demands Of Artificial Intelligence And Big Data
Forbes
The Tokyo Institute of Technology Global Scientific Information (Tokyo Tech) and Computing Center (GSIC) have begun the development and construction of a supercomputer that's expected to meet the demands of artificial intelligence (AI) and big data ...

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This New Supercomputer Will Help Meet Demands Of Artificial Intelligence And Big Data - Forbes

If I Only Had a Brain: How AI ‘Thinks’ – Daily Beast


Daily Beast
If I Only Had a Brain: How AI 'Thinks'
Daily Beast
So can AI, thanks to the development of artificial neural networks (ANN), a type of machine learning algorithm in which nodes simulate neurons that compute and distribute information. AI such as AlphaGo, the program that beat the world champion at Go ...

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If I Only Had a Brain: How AI 'Thinks' - Daily Beast

Artificial intelligence grows a nose | Science | AAAS – Science Magazine

Computer programs odor predictions are on the nose.

Image Source/Alamy Stock Photo

By Robert F. ServiceFeb. 19, 2017 , 8:15 PM

Predicting color is easy: Shine a light with a wavelength of 510 nanometers, and most people will say it looks green. Yet figuring out exactly how a particular molecule will smell is much tougher. Now, 22 teams of computer scientists have unveiled a set of algorithms able to predict the odor of different molecules based on their chemical structure. It remains to be seen how broadly useful such programs will be, but one hope is that such algorithms may help fragrancemakers and food producers design new odorants with precisely tailored scents.

This latest smell prediction effort began with a recent study by olfactory researcher Leslie Vosshall and colleagues at The Rockefeller University in New York City, in which 49 volunteers rated the smell of 476 vials of pure odorants. For each one, the volunteers labeled the smell with one of 19 descriptors, including fish, garlic, sweet, or burnt. They also rated each odors pleasantness and intensity, creating a massive database of more than 1 million data points for all the odorant molecules in their study.

When computational biologist Pablo Meyer learned of the Rockefeller study 2 years ago, he saw an opportunity to test whethercomputer scientists could use it to predict how people would assess smells. Besides working at IBMs Thomas J. Watson Research Center in Yorktown Heights, New York, Meyer heads something called the DREAM challenges, contests that ask teams of computer scientists to solve outstanding biomedical problems, such as predicting the outcome of prostate cancer treatment based on clinical variables or detecting breast cancer from mammogram data. I knew from graduate school that olfaction was still one of the big unknowns, Meyer says. Even though researchers have discovered some 400 separate odor receptors in humans, he adds, just how they work together to distinguish different smells remains largely a mystery.

In 2015, Meyer and his colleagues set up theDREAM Olfaction Prediction Challenge. They divided the Rockefeller groups data set into three parts. Participants were given the volunteer ratings for two-thirds of the odors, along with the chemical structure of the molecules that produced them. They were also given more than 4800 descriptors for each molecule, such as the atoms included, their arrangement, and geometry, which constituted a separate set of more than 2 million data points. These data were then used to train their computer models in predicting smells from chemical structural information. The remaining groups of datatwo sets of 69 ratings and their corresponding chemical informationwere used to test how well the models predicted both how an average person would rate an odor and how each of the 49 individuals would rate them.

Twenty-two teams from around the globe took up the challenge. Many did well, but two stood out. A team led by Yuanfang Guan, a computer scientist at the University of Michigan in Ann Arbor, scored best at predicting how individual subjects rate smells. Another team led by Richard Gerkin at Arizona State University in Tempe best predicted how all the participants on average would rate smells, Meyer and his colleagues report today in Science.

We learned that we can very specifically assign structural features to descriptions of the odor, Meyer says. For example, molecules with sulfur groups tend to produce a garlicky smell, and molecules with a similar chemical structure to vanillin, from vanilla beans, predicts whether subjects will perceive a bakery smell.

Meyer suggests such models may help fragrance and flavor companies come up with new molecules tuned to trigger particular smells, such as sandalwood or citrus. But Avery Gilbert, a biological psychologist at Synesthetics in Fort Collins, Colorado, and a longtime veteran of the fragrance and flavor industry, says hes not so sure. Gilbert says the new work is useful in that it provides such a large data set. But the 19 different verbal descriptors of different scents, he says, is too limited. Thats really a slim number of attributes, he says. Alternative studies have had volunteers use 80 or more categories to rate different smells.

The upshot is that even though the current study showed computers can predict which of 19 words people will use to describe this set of odors, its not clear whetherthe same artificial intelligence programs would rise to the challenge if there were more categories. If you had different descriptors, you might have had different models predict them best. So Im not sure where that leaves us, Gilbert says. Perhaps it serves mostly as a reminder that odor perception remains a challenge both for human scientists and artificial intelligence.

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Alan Turing Predicts Machine Learning And The Impact Of Artificial Intelligence On Jobs – Forbes


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Alan Turing Predicts Machine Learning And The Impact Of Artificial Intelligence On Jobs
Forbes
This week's milestones in the history of technology include Alan Turing anticipating today's deep learning by intelligent machines and concerns about the impact of AI on jobs, Clifford Stoll anticipating Mark Zuckerberg, and establishing the FCC and NPR.

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Alan Turing Predicts Machine Learning And The Impact Of Artificial Intelligence On Jobs - Forbes

MD Anderson Benches IBM Watson In Setback For Artificial – Forbes – Forbes


Forbes
MD Anderson Benches IBM Watson In Setback For Artificial - Forbes
Forbes
MD Anderson has placed a much-ballyhooed 'Watson for cancer' product it was developing with IBM on hold -- and is looking for a new partner.
IBM sees Watson as a primary care provider's assistantZDNet

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Artificial intelligence helps schedule service appointments – Automotive News (subscription) (blog)

"Mandy Monroe" works relentlessly to reach out to customers of Lee Kia of Greenville in North Carolina to schedule service appointments.

A consistent revenue generator, Mandy doesn't complain, gossip, make personal phone calls on company time or pop chewing gum. Mandy doesn't even take bathroom breaks.

That's because Mandy is a cyberstaffer driven by artificial intelligence. The virtual assistant, created by the software provider Conversica, conducts email conversations with dealership service customers.

In a typical exchange, a Lee Kia customer gets an email that greets him or her by name and continues: "My name is Mandy Monroe and I wanted to make sure you got in touch with the Service Department about the first service on your Sportage. Would you like to schedule a service appointment?"

If the response is positive, the program forwards the lead to a service adviser or directs the customer to a scheduling tool on the dealership's website.

Lee Kia began using Conversica's software in mid-2016, says Chad Miller, the dealership's Internet director. The program sent email solicitations to 4,235 service customers last year, he says, and 18 percent responded favorably.

"Before Conversica, we had no way of determining actual engagement with a customer unless each representative kept a sheet of how many calls they made, if they talked to someone, if they left a message," Miller told Fixed Ops Journal.

The average Lee Kia service customer spends $130 per visit, Miller says. "For every 100 customers, if 18 of them are from Conversica, you could see about $2,340 in service from customer pay," he says.

The virtual assistant also enables Lee Kia's service appointment coordinator to handle tasks that require human contact and judgment, such as helping customers get rides home or to work on the dealership's shuttle, Miller says.

Mandy, meet Tiffany

About 45 dealerships have bought the virtual service product since its introduction last year, says Conversica CEO Alex Terry. For dealerships that also use the company's sales platform, the service assistant software costs $499 a month. Dealerships that buy the service software by itself pay $1,500 a month.

Mercedes-Benz of Plano, a Dallas area dealership, uses the Conversica sales platform and plans to add its virtual service assistant by midyear.

Joseph Davis, the dealership's Internet director and an adviser to Conversica, says he's eager to get started.

Davis says the sales tool helps keep the dealership's sales representatives and lead-generation vendors accountable. The dealership calls its virtual sales assistant "Tiffany Ava."

"The [tool] will send me an alert as to how the lead was handled," he says. "Within minutes of the client saying, "No, I haven't gotten what I need,' I am automatically alerted and so are all the team members."

Davis says he expects similar benefits from the service tool.

Other vendors that provide or are developing service, sales, and finance and insurance technology driven by artificial intelligence include CarLabs, ELEAD1ONE, Edmunds.com and Facebook.

Artificial sweetener

Artificial-intelligence assistants and service managers make great teams, Conversica's Terry says.

"Who's the busiest person in the dealership? It's usually the manager of the service department," he says. "Service managers would like to provide a great, engaging experience to the customer, but in real life they don't always respond right away or at all. And it's even more work to do it in a polite and consistent way."

A sample email from a virtual assistant to a potential service customer

The service assistant software uses algorithms that interpret customers' messages and can improve over time as the dealership collects more information, Terry says.

"Over a dozen dealerships told us, "Where I make all my money is the service side of the business,'" Terry says. "We worked collaboratively with the customers and found out what specific challenges they would like the assistant to tackle."

Dealers configure the software, which can work with more than 40 customer relationship management systems, during a setup process that typically takes three or four days.

Terry concedes that artificial intelligence "is not perfect. It also knows when it's not confident in analyzing the message and gets a human involved."

He says that Conversica has refined the service assistant software to "train the artificial intelligence to know what to say on the outbound message and to understand what people say on their return back."

An industry analyst cautions dealers to tread carefully when they bring artificial intelligence into their service operations.

"The key difference from sales is that the vehicle is driving the action," says Ian Mason, senior director of marketing solutions for the information and analytics provider IHS Markit. "The owner doesn't have to go to the selling dealership."

Mason says an artificial intelligence tool can boost a dealership's service business but warns that service department capacity doesn't necessarily scale well. That can mean longer lines in the service drive, he adds.

"Long waits aren't a good thing for the dealership," he says. When dissatisfied customers use social media, he notes, "Word of mouth is a lot faster and gets out very quickly. It's almost uncontrollable."

Even as dealerships' use of artificial intelligence software grows, some responses remain best left to humans.

Davis says that Tiffany, his dealership's virtual sales assistant, is evidently so gifted at email conversation that at least two would-be suitors have shown up at the store with candy and flowers, hoping to meet her and ask her for a date.

"We just tell them she's an outside assistant on the Internet team," he says.

AI, A to Z

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Why are Indian engineers so afraid of ‘artificial intelligence’? – Scroll.in

Artificial intelligence is being counted among the hottest startup sectors in India this year, but the highly specialised space is struggling to grow due to the lack of a primary input: engineers.

Forget getting people of our choice, we dont even get applications when we advertise for positions for our AI team, said 25-year-old Tushar Chhabra, co-founder of Cron Systems, which builds internet of things-related solutions for the defence sector. Its as if people are scared of the words artificial intelligence. They start freaking out when we ask them questions about AI.

India has over 170 startups focused purely on AI, which have together raised over $36 million. The sector has received validation from marquee investors like Sequoia Capital, Kalaari Capital, and business icon Ratan Tata. But entrepreneurs are struggling to expand due to a shortage of engineers with skills related to robotics, machine learning, analytics, and automation.

Racetrack.ai co-founders Subrat Parida and Navneet Gupta said that around 40% of their working time is spent searching for the right talent. The organisation, which operates out of Bengaluru, has built an AI-driven communication bot called Marvin. People are the core strength of a startup, Parida, also the CEO, told Quartz. So hiring for a startup is very challenging. We are not looking for the regular tech talent and, since AI is a relatively new field in India, you dont get people with past experience in working on those technologies.

Only 4% of AI professionals in India have actually worked on cutting-edge technologies like deep learning and neural networks, which are the key ingredients for building advanced AI-related solutions, according to recruitment startup Belong, which often helps its clients discover and recruit AI professionals.

Also, many such companies require candidates with PhD degrees in AI-related technologies, which is rare in India.

While it takes a company just a month to find a good app developer, it could take up to three months to fill up a position in the AI space, said Harishankaran K, co-founder and CTO of HackerRank, which helps companies hire tech talent through coding challenges.

India is among the top countries in terms of the number of engineers graduating every year. But the engineering talent here has traditionally been largely focused on IT and not research and innovation.

Fields like AI require a mindset of research and experimentation, said PK Viswanathan, professor of business intelligence at the Great Lakes Institute of Management in Chennai. But most aspiring engineers in India follow a pattern: finish school, go to IIT, do an MBA, and then take up a job. To work on AI, you need people who not only have a strong technology background, but also have analytical thinking, puzzle-solving skills, and they should not be scared of numbers.

Ironically, the subject has been a part of the curriculum at some engineering schools for almost a decade. However, what is taught there is mostly irrelevant to the real world.

Sachin Jaiswal, who graduated from IIT Kharagpur in 2011, studied some aspects of AI back in college. But whatever he is doing at his two-year-old startup Niki.ai it has built a bot that lets users order anything through a chat interface is based on what he learned in his earlier jobs, he said.

A lot of people are disillusioned when they come out of college and begin their first jobs, said Jaiswal, whose startup is backed by Ratan Tata.

In fact, even now, when he interacts with graduates from elite institutes to hire them, he sees a glaring gap between what these youngsters have learned and what is needed on the work floor.

Given the shortage of AI-related talent in India, several startups aspire to tap Silicon Valley. But thats not a feasible solution for young teams.

A few months back, Chhabra of Cron Systems was in talks with a US-based engineer, an IIT-Delhi alumnus working on AI for seven years. The guy asked for Rs 2.5 crore per annum as salary, said Chhabra. As a startup you cannot afford that price.

Cron Systems has found a jugaad to solve their problem, Chhabra said. Late last year, the company hired a bunch of engineers with basic skills needed to create AI-related solutions and trained them.

We broke down AI into smaller pieces and hired six tech professionals who understood those basic skills well, Chhabra said. Then we conducted a three-month training for these people and brought them onboard with what we do.

Niki.ai, too, is following this hire-and-train model. Training takes time and investment but we have no option because we need the talent, Jaiswal of Niki.ai told Quartz. If we had better access to talent, things would have been better.

Gurugram-based AI startup Staqu has started partnering with academic institutions to build a steady pipeline of engineers and researchers.

Despite this struggle, entrepreneurs and investors in India feel bullish.

In an ecosystem where e-commerce and food delivery hog the limelight, a recent report by venture lending firm InnoVen Capital named AI one of the most under-hyped sectors. But that is set to change, said London-based angel investor Sanjay Choudhary.

In September, Choudhary invested in Delhi-based AI startup Corseco Technologies. He regularly interacts with the companys team and the genuine issue of finding talent comes up frequently, he told Quartz.

India is a late entrant into the AI space and talent crunch will be a challenge for the industry for some time to come, he said. But I plan to continue investing in AI in India because I feel that the space has a lot of potential and needs to be supported.

While there seems no end to the struggle, Jaiswal of Niki.ai sees a silver lining: Talent crunch ensures that companies cant enter the field easily. So we have a competitive edge.

This article first appeared on Quartz.

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Banco Santander Invests in Artificial Intelligence Startups – Fortune

When learning becomes cognition. Photograph by Agliolo Mike Getty Images/Photo Researchers RM

Spanish lender Banco Santander SA has invested in two artificial-intelligence companies, part of the financial industry's increased focus on technology smart enough to mimic human thinking, sources familiar with the deals told Reuters.

The bank's venture arm, Santander InnoVentures, bought stakes in Personetics Technologies, which provides automated customer service, and Gridspace, whose software can learn and interpret language the way a person would, the sources said.

The size of the investments could not be determined and the sources asked not to be named because they were not allowed to disclose the information publicly.

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The deals underscore how lenders have become more interested in using artificial intelligence for a wide variety of tasks, including hiring, spotting fraud, improving call centers and recommending products for customers.

Personetics, which has offices in New York, London and Tel Aviv, creates "chatbots" that can respond to customer questions through popular messaging platforms like Facebook's ( fb ) Messenger. French bank Societe Generale, for instance, is using Personetics to answer queries about equity funds in its Romanian banking unit.

San Francisco-based Gridspace's technology can be used by banks to monitor conversations between customers and employees at call centers to improve service.

Its Time to Hire a Chief AI Officer

Santander ( san ) set up its London-based InnoVentures group in 2014 to invest in young financial-technology companies that can improve its digital offerings. The division was initially allocated $100 million to invest but was given an extra $100 million in July.

It has backed more than a dozen companies so far, including automated wealth manager SigFig, Swedish payments company iZettle and Digital Asset Holdings, which develops blockchain software and is led by former JPMorgan Chase & Co ( jpm ) commodities chief Blythe Masters.

Other global banks have venture units with similar remits. Santander InnoVentures was among the most active bank-owned investors in fintech companies last year, alongside Goldman Sachs Group ( gs ) and Citigroup ( c ) , according to data by CB Insights.

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Artificial Intelligence & Bias – Huffington Post

By Jackson Grigsby, Harvard Class of 2020

On Thursday, February 16th, the JFK Jr. Forum at the Harvard Institute of Politics hosted a conversation on the past, present, and future of Artificial Intelligence with Harvard Kennedy School Professor of Public Policy Iris Bohnet, Harvard College Gordon McKay Professor of Computer Science Cynthia Dwork, and Massachusetts Institute of Technology Professor Alex Sandy Pentland.

Moderated by Sheila Jasanoff, Kennedy School Pforzheimer Professor of Science and Technology Studies, the conversation focused on the potential benefits of Artificial Intelligence as well as some of the major ethical dilemmas that these experts predicted. While Artificial Intelligence (AI) has the potential to eliminate inherent human bias in decision-making, the panel agreed that in the near future, there are ethical boundaries that society and governments must explore as Artificial Intelligence expands into the realms of medicine, governance, and even self-driving cars.

Some major takeaways from the event were:

1. Artificial Intelligence offers an incredible opportunity to eliminate human biases in decision-making

In the future, Artificial Intelligence can be utilized to eliminate inherent human biases that often influence important decisions surrounding employment, government policy, and even policing. At the event, Professor Iris Bohnet stated that every person has biases that inform their decisions. These biases can affect whether a candidate for a job is chosen or not. As a result, Bohnet suggested that by using algorithms, employers could choose the best candidates by using AI to focus on the candidates qualifications rather than by basing decisions on gender, race, age or other variables. However, the panel also discussed the fact that even algorithms can have bias. For example, the algorithm that is used to match medical students with residency hospitals can either be biased in favor of the hospitals preferences or the students. It is up to humans to control bias in the algorithms that they use.

2. Society must begin having conversations surrounding the ethics of Artificial Intelligence

Due to the fact that Artificial Intelligence is becoming more popularly utilized, society and governments must continue to have conversations addressing ethics and Artificial Intelligence. Professors Alex Pentland and Cynthia Dwork stated that as Artificial Intelligence proliferates, moral conflicts can surface. Pentland emphasized that citizens must ask themselves is this something that is performing in a way that we as a society want? Pentland noted that our society must continue a dialogue around ethics and determine what is right.

3. Although Artificial Intelligence is growing, there are still tasks that only humans should do

In the end, the experts agreed, there are tasks and decisions that only humans can make. At the same time, there are some tasks and decisions that could be executed by machines, but ultimately should be done by humans. Professor Bohnet emphasized this point by reaffirming humanitys position, concluding, There are jobs that cannot be done by machines.

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Artificial Intelligence & Bias - Huffington Post

The new IQ test: Technologists assess the potential of artificial intelligence – SC Magazine

AI may still seem like a far-flung concept, but in cybersecurity its already a reality.

Rather than focus on attack signatures, these AI solutions look for anomalous network behavior, flagging when a machine goes rogue or if user activity or traffic patterns appear unusual. A really simple example is someone with high privilege who attempts to get onto a system at a time of day or night that they never normally log in and potentially from a geolocation or a machine that they don't log in from, said Kelley.

Another example would be a really rapid transfer of a lot of data, especially if that data consists of the corporate crown jewels.

Such red-flags allow admins to quickly catch high-priority malware infections and network compromises before they can cause irreparable damage.

IBM calls this kind of machine learning cognitive with a little c' which the company was already practicing prior to Watson. Despite its diminutive designation, little c can have some big benefits for one's network.

A network really in its simplest form, is a data set, one that changes with every millisecond, said Justin Fier, director of cyber intelligence and analysis at U.K.-based cybersecurity company Darktrace, whose network threat detection solution was created by mathematicians and machine-learning specialists from the University of Cambridge. With machine learning, we can analyze that data in a more efficient way.

We're not looking for malicious behavior, we're looking for anomalous behavior, Fier continued, in an interview with SC Media. And that can sometimes turn into malicious behavior and intent, or it can turn into configuration errors or it could just be vulnerable protocols. But we're looking for the things that just stand out.

An advantage of these kinds of AI solutions is that they often run on unsupervised learning models meaning they do not need to be fed scores of data in advance to help its algorithms define what constitutes a true threat. Rather, they tend to self-learn through observation, making note of which machines are defying typical patterns a process that Fier said is the AI determining its own sense of self on the network.

While Fier said that basic compliance failures are the most commonly detected issue, he recalled one particular client that used biometric fingerprint scanners for security access, only to discover through anomaly detection that one of these devices had been connected to the Internet and subsequently breached.

To cover up his activity, the perpetrator modified and deleted various log files, but this unusual behavior was discovered as well. The solution even found irregularities in the network server that suggested the culprit moved fingerprint data from the biometric device to a company database, perhaps to establish an alibi. My belief is that somebody on the inside was probably getting get help from somebody on the outside, said Fier, noting that it was a significant find because insider threats are one of the hardest things to catch.

Another client, Catholic Charities of Santa Clara County, an affiliate of CatholicCharities USA that helps 54,000 local clients per year, used anomaly detection to thwart an attempted ransomware attack only weeks after commencing a test of the technology. The solution immediately flagged the event, after a receptionist opened a malicious email with a fake invoice attachment. I was able to respond right away, and disconnected the targeted device to prevent any further encryption or financial cost, saidWill Bailey, director of IT at the social services organization.

Little c's benefits extend beyond the network as well. Kelley cited the advent of application scanning tools that seek out problematic lines of code in websites and mobile software that could result in exploitation. And Fier noted a current Darktrace endeavor called Project Turing, whereby researchers are using AI to model how security analysts and investigators work in order to make their jobs more efficient.

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The new IQ test: Technologists assess the potential of artificial intelligence - SC Magazine

Artificial Intelligence In Schools Is Closer Than You Think – Forbes

Artificial Intelligence In Schools Is Closer Than You Think
Forbes
Education stands to benefit from rapid developments in artificial intelligence. But historically, adaptive learning software has been programmed in a top-down fashion. It asks a question, and if the child provides a particular answer, a set of prompts ...

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Artificial Intelligence In Schools Is Closer Than You Think - Forbes

Indian engineers need to stop being so afraid of the term artificial intelligence – Quartz

Artificial intelligence (AI) is being counted (pdf) among the hottest startup sectors in India this year, but the highly specialised space is struggling to grow due to the lack of a primary input: engineers.

Forget getting people of our choice, we dont even get applications when we advertise for positions for our AI team, said 25-year-old Tushar Chhabra, co-founder of Cron Systems, which builds internet of things (IOT)-related solutions for the defence sector. Its as if people are scared of the words artificial intelligence. They start freaking out when we ask them questions about AI.

India has over 170 startups focused purely on AI, which have together raised over $36 million. The sector has received validation from marquee investors like Sequoia Capital, Kalaari Capital, and business icon Ratan Tata. But entrepreneurs are struggling to expand due to a shortage of engineers with skills related to robotics, machine learning, analytics, and automation.

Racetrack.ai co-founders Subrat Parida and Navneet Gupta say that around 40% of their working time is spent searching for the right talent. Bengaluru-based Racetrack.ai has built an AI-driven communication bot called Marvin. People are the core strength of a startup. So hiring for a startup is very challenging. We are not looking for the regular tech talent and, since AI is a relatively new field in India, you dont get people with past experience in working on those technologies, Parida, also the CEO, told Quartz.

Only 4% of AI professionals in India have actually worked on cutting-edge technologies like deep learning and neural networks, which are the key ingredients for building advanced AI-related solutions, according to recruitment startup Belong, which often helps its clients discover and recruit AI professionals.

Also, many such companies require candidates with PhD degrees in AI-related technologies, which is rare in India.

While it takes a company just a month to find a good app developer, it could take up to three months to fill up a position in the AI space, said Harishankaran K, co-founder and CTO of HackerRank, which helps companies hire tech talent through coding challenges.

India is among the top countries in terms of the number of engineers graduating every year. But the engineering talent here has traditionally been largely focused on IT and not research and innovation.

Fields like AI require a mindset of research and experimentation. But most aspiring engineers in India follow a pattern: finish school, go to IIT, do an MBA, and then take up a job, said PK Viswanathan, professor of business intelligence at the Great Lakes Institute of Management in Chennai. To work on AI, you need people who not only have a strong technology background, but also have analytical thinking, puzzle-solving skills, and they should not be scared of numbers, he added.

Ironically, the subject has been a part of the curriculum at some engineering schools for almost a decade. However, what is taught there is mostly irrelevant to the real world.

Sachin Jaiswal, who graduated from IIT Kharagpur in 2011, studied some aspects of AI back in college. But whatever he is doing at his two-year-old startup Niki.aiit has built a bot that lets users order anything through a chat interfaceis based on what he learned in his earlier jobs, he said.

A lot of people are disillusioned when they come out of college and begin their first jobs, said Jaiswal, whose startup is backed by Ratan Tata.

In fact, even now, when he interacts with graduates from elite institutes to hire them, he sees a glaring gap between what these youngsters have learned and what is needed on the work floor.

Given the shortage of AI-related talent in India, several startups aspire to tap Silicon Valley. But thats not a feasible solution for young teams.

A few months back, Chhabra of Cron Systems was in talks with a US-based engineer, an IIT-Delhi alumnus working on AI for seven years. The guy asked for Rs2.5 crore per annum as salary. As a startup you cannot afford that price, said Chhabra.

Cron Systems has found a jugaad to solve their problem, Chhabra said. Late last year, the company hired a bunch of engineers with basic skills needed to create AI-related solutions and trained them.

We broke down AI into smaller pieces and hired six tech professionals who understood those basic skills well. Then we conducted a three-month training for these people and brought them onboard with what we do, Chhabra said.

Niki.ai, too, is following this hire-and-train model. Training takes time and investment but we have no option because we need the talent, Jaiswal of Niki.ai told Quartz. If we had better access to talent, things would have been better.

Gurugram-based AI startup Staqu has started partnering with academic institutions to build a steady pipeline of engineers and researchers.

Despite this struggle, entrepreneurs and investors in India feel bullish.

In an ecosystem where e-commerce and food delivery hog the limelight, a recent report by venture lending firm InnoVen Capital named AI one of the most under-hyped sectors. But that is set to change, said London-based angel investor Sanjay Choudhary.

In September 2016, Choudhary invested in Delhi-based AI startup Corseco Technologies. He regularly interacts with the companys team and the genuine issue of finding talent comes up frequently, he told Quartz.

India is a late entrant into the AI space and talent crunch will be a challenge for the industry for some time to come, he said. But I plan to continue investing in AI in India because I feel that the space has a lot of potential and needs to be supported.

While there seems no end to the struggle, Jaiswal of Niki.ai sees a silver lining: Talent crunch ensures that companies cant enter the field easily. So we have a competitive edge.

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Indian engineers need to stop being so afraid of the term artificial intelligence - Quartz

‘Uncanny Valley’ explores human experience through artificial intelligence – Laramie Boomerang

A production by a local theater troupe looks to explore questions about science and technology while cutting to the core of what it means to be human.

Uncanny Valley, a presentation of Laramie-based Relative Theatrics, premiered Wednesday and continues with performances through Feb. 25, including today. Performances begin at 7:30 p.m. in the Gryphon Theatre, 710 Garfield St. Tickets are $10 in advance or $15 on the day of the show. Student and senior discounts are available for Thursdays show for $8. Tickets are available at http://www.gryphontheatre.org or at the Laramie Plains Civic Center offices from 9 a.m.-4 p.m. Monday-Friday.

Director James Hockenberry said the play is about the relationship between neuroscientist Claire, played by Alison Harkin, and the non-biological being Julian, played by Ryan Archibald. Though it covers the newsworthy topic of artificial intelligence, Hockenberry said the plays subject matter is more about the human experience.

The flashy bit is about artificial intelligence, but really its about how they relate to each other and how she teaches him what it means to be human, and the fact thats not all good stuff, he said. Theres quite a bit of negative in what it means to be human and go through life interacting with other people. She doesnt mean to reveal some of that to him, but he sort of picks up on it.

With an audience living in a world where artificial intelligence technology is a reality, Hockenberry said the plays final act begs the question of where humanity goes from here.

In a world where A.I. is possible or coming around the corner maybe even here whats the next step for humanity? he asked. If we havent figured out those issues of dealing with ourselves, are we really ready for the next step?

The mission statement for Relative Theatrics includes making sure performances are risky and relevant, Hockenberry said. With contemporarily relevant and controversial subject matter, he said Uncanny Valley was an obvious choice for the theater troupe.

This play is incredibly topical despite being set 40-some-odd-years in the future, Hockenberry said. It deals with income inequality, it deals with issues of power and honesty and basic human needs, the relationships between family members it even features a billionaire, which is a big topic in America right now.

Though hes a self-described science fiction fan, Hockenberry said he really appreciates the way Uncanny Valley explores profound questions that arent bound by the subject matter of the play.

I definitely got my fill of tech jargon, but at the same time, what really drew me to it are those human elements, which are timeless, he said.

One of the performances unique aspects is that the audience joins the actors on the stage. Tickets are limited to 50 per show to fit audience members on the same plane as the actors, which Hockenberry said allows for an experience unique to sitting in chairs off-stage.

Its a very intimate viewing experience, he said. We have so many people that hear that, and their just trained I sit in the audience and the stage happens somewhere else. With this, theyll be right up there in it. While were not interactive in the way a comedy show might, its impossible to not feel connected to the action when youre so close to it. When you can hear the intake of breath before a line, I think thats what Id say to someone whos never experience Relative Theatrics before, youve maybe never experienced anything like this.

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'Uncanny Valley' explores human experience through artificial intelligence - Laramie Boomerang

Artificial intelligence and the promise of a changing federal landscape – Washington Technology

EMERGING TECH

The future of federal IT belongs to CIOs who can build flexible, nimble organizations able to maximize the advantage of existing technologies like cloud services and automated machine intelligence while laying the groundwork for a range of emerging technologies on the horizon.

Thats according to a new report on government technology trends for 2017 published Wednesday by Deloitte. Researchers identified eight technologies they believe have an opportunity to disrupt and change the way the federal government leverages information, data and software over the next two years.

Some are a continuation of existing trends that are already established, like IT consolidation and greater reliance on cloud-based software and services. Others, like artificial intelligence, mixed reality and nanotechnology veer more into the outer edges of what is currently possible today, but may have far more relevance a few years down the line.

Scott Buchholtz, director of systems integration at Deloitte, said he is optimistic that the changing federal landscape will provide both the necessary space and incentive for CIOs to start thinking beyond their old legacy architectures.

I believe that some of the changing demographics in the marketplace, some of the restrictions on budgets that were likely to see and some of the convergence going on are likely to make government more open to automation and the role of technologythat a lot of our commercial clients have been using for years, said Buchholtz.

That includes tools like artificial intelligence, machine learning, along with virtual and augmented reality. Buccholtz said these still-nascent technologies have the potential for broad application in federal IT, but need more trailblazers willing to create successful and relevant test cases.

Last year, the Obama administration encouraged agencies to create their own high-risk, high-reward research on AI, remarking, the walls between humans and AI systems are slowly beginning to erode. Last October, the General Services Administration launched new digital communities to provide agency guidance on how to incorporate AI and mixed reality.

According to Deniece Peterson, director of federal market analysis at Deltek (disclosure: the author previously worked at Deltek), this has set the stage for IT managers to start laying the groundwork for some these technologies in 2017 and begin pilot and test programs to build a case for broader adoption down the line.

When it comes to dollars, a lot of this is stuff thats popping up in R&D [budgets] so when they want to expand it, they have an example to point to, Peterson said.

Breaking down the silos between IT and the agencies they serve is another trend that is expected to accelerate over the next two years. Building on past consolidation efforts, IT unbounded is Deloittes term for the process federal CIOs are using to change their operations in order to better match the nimble, adaptable nature of their private sector counterparts.

These efforts might get a boost in the form of a new president who hails from the private sector and has often spoke of making government work more like a business.

I believe that the new administration is placing a very different focus than has traditionally been the case on technology and management of technology, Buchholtz said. Were still in the early days, so it remains to be seen, but there are many indications that theres going to be a much higher expectation for outcomes and results, particularly in the technology space.

Peterson said a Trump administration may have the will to change the way federal IT works, but bureaucratic red tape and the long-term nature of the budget, appropriations and contracting cycles perpetually leave agencies dealing with yesterdays technology solutions.

Absent passage of legislative reform such as the Modernizing Government Technology Act, that process is likely to continue hampering efforts to make the federal government more nimble.

If the Trump administrations intent could meet with Congress passing [IT modernization reform], it will be a step in the right direction, said Peterson.

Rounding out Deloittes other trends are a greater reliance on inevitable architecture like cloud-based services and automated technologies that have been steadily gaining traction over the past few years, along with a list of exponential technologies like quantum computing, nanotechnology and biotechnology. These are the tools that Deloitte thinks will form the foundations of the modern IT architecture. To get there, Buchholtz said IT managers will need the freedom to think outside the box and discard the risk-adverse mindset that currently dominates federal decision making.

I think its important to realize that we collectively as a country have created an environment where failed experiments are punished disproportionately to success, Buchholtz said. We need to figure out how to better enable those with vision to fail small and fail quickly, but also get up, keep going and learn [the necessary] lessons.

About the Author

Derek B. Johnson is a freelance writer.

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Artificial intelligence and the promise of a changing federal landscape - Washington Technology

China’s Artificial-Intelligence Boom – The Atlantic

Each winter, hundreds of AI researchers from around the world convene at the annual meeting of the Association of the Advancement of Artificial Intelligence. Last year, a minor crisis erupted over the schedule, when AAAI announced that 2017s meeting would take place in New Orleans in late January. The location was fine. The dates happened to conflict with Chinese New Year.

The holiday might not have been a deal breaker in the past, but Chinese researchers have become so integral to the meeting, it could not go on without them. They had to reschedule. Nobody would have put AAAI on Christmas day, says current AAAI president Subbarao Kambhampati. Our organization had to almost turn on a dime and change the conference venue to hold it a week later.

The 2017 AAAI meetingwhich ultimately relocated to San Franciscowrapped up just last week. And as expected, Chinese researchers had a strong showing in the historically U.S.-dominated conference. A nearly equal number of accepted papers came from researchers based in China and the U.S. This is pretty surprising and impressive given how different it was even three, four years back, says Rao.

Chinas rapid rise up the ranks of AI research has people taking notice. In October, the Obama White House released a strategic plan for AI research, which noted that the U.S. no longer leads the world in journal articles on deep learning, a particularly hot subset of AI research right now. The country that had overtaken the U.S.? China, of course.

Its not just academic research. Chinese tech companies are betting on AI, too. Baidu (a Chinese search-engine company often likened to Google), Didi (often likened to Uber), and Tencent (maker of the mega-popular messaging app WeChat) have all set up their own AI research labs. With millions of customers, these companies have access to the huge amount of data that training AI to detect patterns requires.

Like the Microsofts and Googles of the world, Chinese tech companies see enormous potential in AI. It could undergird a whole set of transformative technologies in the coming decades, from facial recognition to autonomous cars.I have a hard time thinking of an industry we cannot transform with AI, says Andrew Ng, chief scientist at Baidu. Ng previously cofounded Coursera and Google Brain, the companys deep learning project. Now he directs Baidus AI research out of Sunnyvale, California, right in Silicon Valley.

* * *

Chinas success in AI has been partly fueled by the governments overall investment in scientific research at its universities. Over the past decade, government spending on research has grown by double digits on average every year. Funding of science and technology research continues to be a major priority, as outlined by the the Five-Year Plan unveiled this past March.

When Rao first started seeing Chinese researchers at international AI meetings, he recalls they were usually from Tsinghua and Peking University, considered the MIT and Harvard of China. Now, he sees papers from researchers all over the country, not just the most elite schools. Machine learningwhich includes deep learninghas been an especially popular topic lately. The number of people who got interested in applied machine learning has tremendously increased across China, says Rao. This is the same uptick that the White House noticed in its report on a strategic plan for AI research.

Chinese tech companies are part of the infusion of research dollars to universities, too. At Hong Kong University of Science and Technology, computer scientist Qiang Yang collaborates with Tencent, which sponsors scholarships for students in his lab.

The students get access to mountains of data from WeChat, the messaging app from Tencent that is akin to Facebook, iMessage, and Venmo all rolled into one. (With AI, they cant do it without a lot of data and a platform to test it on, says Yang, which is why industry collaboration is so key.) In return, Tencent gets a direct line to some of the most innovative research coming out of academic labs. And of course, some of these students end up working at Tencent when they graduate.

The quantity of Chinese AI research has grown dramatically, but researchers in the U.S. are still responsible for a lot of the most fundamental groundbreaking work. The very clever ideas on changing network architecture, I see those in the U.S., says Ng. What Chinese researchers have been very good at doing is seizing on an idealike machine learningand cranking out papers on its different applications.

Yet as the research matures in China, Ng says, it is also becoming its own distinct community. After a recent international meeting in Barcelona, he recalls seeing Chinese language write-ups of the talks circulate right way. He never found any in English. The language issue creates a kind of asymmetry: Chinese researchers usually speak English so they have the benefit of access to all the work disseminated in English. The English-speaking community, on the other hand, is much less likely to have access to work within the Chinese AI community.

China has a fairly deep awareness of whats happening in the English-speaking world, but the opposite is not true, says Ng. He points out that Baidu has rolled out machine translation and voice recognition services powered by AIbut when Google and Microsoft, respectively, did so later, the American companies got a lot more publicity.

And when it comes to actually shipping new features, China companies can move more quickly. The velocity of work is much faster in China than in most of Silicon Valley, says Ng. When you spot a business opportunity in China, the window of time you have to respond usually very shortshorter in China than the United States.

Yang chalks it up to Chinas highly competitive ecosystem. WeChat, for example, has built a set of features around QR codes (yes, really), chat, payments, and friend discovery that make it indispensable to daily life in China. American social media companies only wish they had that kind of loyalty. Product managers at Tencent have good sense of what customers want, and they can can quickly turn technology into reality, says Yang. This cycle is very short. And to stay competitive, theyre primed to integrate AI to improve their products. Whether Chinese tech companies use the AI wave to break into the international market remains to be seenbut theyre already using AI to compete for customers in China.

In the academic world, AAAI has now taken steps to make sure Chinese researchers have input on the meetings. The exact date of Chinese New Year changes every year, but its always in January or February, when the AAAI meeting usually takes place. Cant have them conflicting again.

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China's Artificial-Intelligence Boom - The Atlantic

Are Artificial Intelligence Companies Obliged To Retrain Technologically Displaced Workers? – Forbes


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Are Artificial Intelligence Companies Obliged To Retrain Technologically Displaced Workers?
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Don't technology companies who promote AI as the way forward also have an obligation to retrain our workforce to deal with the coming job disruption? originally appeared on Quora: the place to gain and share knowledge, empowering people to learn from ...

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Are Artificial Intelligence Companies Obliged To Retrain Technologically Displaced Workers? - Forbes

Alphabet’s Eric Schmidt: ‘I Was Proven Completely Wrong’ About Artificial Intelligence – Fortune

While leading Google through the aughts, Eric Schmidt made a miscalculation.

"I was proven completely wrong" about artificial intelligence, Alphabet's executive chairman said at the RSA security conference in San Francisco on Wednesday.

Schmidt has initially skeptical about the technology, and he's since acknowledged how vital it is to both the company's mission and to the global economy.

Indeed, Google ( goog ) CEO Sundar Pichai has described the world as having entered an "AI-first" era. The preceding phase was a focus on all things mobile- and smartphone-first (see: Android), according to Pichai, who succeeded Schmidt after a second CEO stint by Google co-founder Larry Page.

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Schmidt's assessment back then was that artificial intelligence research faced tremendous obstacles that inhibited its progress. He "didn't think it would scale," he said of the machine learning tech.

And he said he also didn't think it would "generalize," meaning becoming more flexible and elastic, like the human mind, rather than remaining a specialized tool suited only to specific tasks.

Schmidt had underestimated the power of simple algorithms to "emulate very complex things," he said, while qualifying that "we're still in the baby stages of doing conceptual learning."

Read more: Forget Artificial Intelligence. Why 'Artificial Stupidity' Is the Real Threat

In other words, computer scientists are still teaching machines to heuristically categorize basic elements of the world: building representations of "things" and "actions" by parsing the components of images (like colors, shapes, and lines), as well as sounds (like tones, pitches, and phonemes).

"General AI," or mimicking the elasticity of human thought, is still decades away from reality, by Schmidt's estimation. But he has become more bullish about the prospect in recent years.

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The moment that changed everything, he said, was the success of a particular Google experiment involving neural networks in 2012. Ironically, Schmidt said, the team's creation didn't uncover some major mathematical breakthrough. Rather, it found something far more mundane.

"You'd think it would have been the discovery of basic set theory," Schmidt said, referring to an esoteric realm of mathematics. "Instead, it was the discovery of cats on YouTube."

Indeed, the Google Brain team had tasked thousands of computer processors with recognizing objects in YouTube video thumbnail images. The resultan ability to distinguish catshelped launch a wave of renewed interest in the field of deep learning. (For more on that tech revolution, read this recent Fortune feature .)

As far as questions about apocalyptic scenarios, like a robot uprising, Schmidt echoed statements he has made in the past by throwing water on the alarmists. "These are important philosophical questions, but ones that we're not facing right now," he said.

In Schmidt's view, the positives far outweigh the negatives for AI. "Things that bedevil us, like traffic accidents and medical diagnoses will get better," he said.

"I will stake my reputation that that will be the real narrative over the next five years."

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Alphabet's Eric Schmidt: 'I Was Proven Completely Wrong' About Artificial Intelligence - Fortune