Daily Archives: July 27, 2017

Designed by Pierpaolo Lazzarini from Italian company Jet Capsule. The I.F.O. is fuelled by eight electric engines, which is able to push the flying object to an estimated top speed of about 120mph.

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A humanoid robot gestures during a demo at a stall in the Indian Machine Tools Expo, IMTEX/Tooltech 2017 held in Bangalore

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A humanoid robot gestures during a demo at a stall in the Indian Machine Tools Expo, IMTEX/Tooltech 2017 held in Bangalore

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Engineers test a four-metre-tall humanoid manned robot dubbed Method-2 in a lab of the Hankook Mirae Technology in Gunpo, south of Seoul, South Korea

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Engineers test a four-metre-tall humanoid manned robot dubbed Method-2 in a lab of the Hankook Mirae Technology in Gunpo, south of Seoul, South Korea

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The giant human-like robot bears a striking resemblance to the military robots starring in the movie 'Avatar' and is claimed as a world first by its creators from a South Korean robotic company

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Engineers test a four-metre-tall humanoid manned robot dubbed Method-2 in a lab of the Hankook Mirae Technology in Gunpo, south of Seoul, South Korea

Jung Yeon-Je/AFP/Getty Images

Waseda University's saxophonist robot WAS-5, developed by professor Atsuo Takanishi

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Waseda University's saxophonist robot WAS-5, developed by professor Atsuo Takanishi and Kaptain Rock playing one string light saber guitar perform jam session

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A test line of a new energy suspension railway resembling the giant panda is seen in Chengdu, Sichuan Province, China

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A test line of a new energy suspension railway, resembling a giant panda, is seen in Chengdu, Sichuan Province, China

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A concept car by Trumpchi from GAC Group is shown at the International Automobile Exhibition in Guangzhou, China

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A Mirai fuel cell vehicle by Toyota is displayed at the International Automobile Exhibition in Guangzhou, China

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A visitor tries a Nissan VR experience at the International Automobile Exhibition in Guangzhou, China

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A man looks at an exhibit entitled 'Mimus' a giant industrial robot which has been reprogrammed to interact with humans during a photocall at the new Design Museum in South Kensington, London

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A new Israeli Da-Vinci unmanned aerial vehicle manufactured by Elbit Systems is displayed during the 4th International conference on Home Land Security and Cyber in the Israeli coastal city of Tel Aviv

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Electrification Guru Dr. Wolfgang Ziebart talks about the electric Jaguar I-PACE concept SUV before it was unveiled before the Los Angeles Auto Show in Los Angeles, California, U.S

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The Jaguar I-PACE Concept car is the start of a new era for Jaguar. This is a production preview of the Jaguar I-PACE, which will be revealed next year and on the road in 2018

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Japan's On-Art Corp's CEO Kazuya Kanemaru poses with his company's eight metre tall dinosaur-shaped mechanical suit robot 'TRX03' and other robots during a demonstration in Tokyo, Japan

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Japan's On-Art Corp's eight metre tall dinosaur-shaped mechanical suit robot 'TRX03'

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Japan's On-Art Corp's eight metre tall dinosaur-shaped mechanical suit robot 'TRX03' performs during its unveiling in Tokyo, Japan

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Singulato Motors co-founder and CEO Shen Haiyin poses in his company's concept car Tigercar P0 at a workshop in Beijing, China

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The interior of Singulato Motors' concept car Tigercar P0 at a workshop in Beijing, China

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Singulato Motors' concept car Tigercar P0

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A picture shows Singulato Motors' concept car Tigercar P0 at a workshop in Beijing, China

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Connected company president Shigeki Tomoyama addresses a press briefing as he elaborates on Toyota's "connected strategy" in Tokyo. The Connected company is a part of seven Toyota in-house companies that was created in April 2016

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A Toyota Motors employee demonstrates a smartphone app with the company's pocket plug-in hybrid (PHV) service on the cockpit of the latest Prius hybrid vehicle during Toyota's "connected strategy" press briefing in Tokyo

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An exhibitor charges the battery cells of AnyWalker, an ultra-mobile chasis robot which is able to move in any kind of environment during Singapore International Robo Expo

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A robot with a touch-screen information apps stroll down the pavillon at the Singapore International Robo Expo

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An exhibitor demonstrates the AnyWalker, an ultra-mobile chasis robot which is able to move in any kind of environment during Singapore International Robo Expo

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Robotic fishes swim in a water glass tank displayed at the Korea pavillon during Singapore International Robo Expo

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An employee shows a Samsung Electronics' Gear S3 Classic during Korea Electronics Show 2016 in Seoul, South Korea

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Visitors experience Samsung Electronics' Gear VR during the Korea Electronics Grand Fair at an exhibition hall in Seoul, South Korea

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Amy Rimmer, Research Engineer at Jaguar Land Rover, demonstrates the car manufacturer's Advanced Highway Assist in a Range Rover, which drives the vehicle, overtakes and can detect vehicles in the blind spot, during the first demonstrations of the UK Autodrive Project at HORIBA MIRA Proving Ground in Nuneaton, Warwickshire

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Chris Burbridge, Autonomous Driving Software Engineer for Tata Motors European Technical Centre, demonstrates the car manufacturer's GLOSA V2X functionality, which is connected to the traffic lights and shares information with the driver, during the first demonstrations of the UK Autodrive Project at HORIBA MIRA Proving Ground in Nuneaton, Warwickshire

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Ford EEBL Emergency Electronic Brake Lights is demonstrated during the first demonstrations of the UK Autodrive Project at HORIBA MIRA Proving Ground in Nuneaton, Warwickshire

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Full-scale model of 'Kibo' on display at the Space Dome exhibition hall of the Japan Aerospace Exploration Agency (JAXA) Tsukuba Space Center, in Tsukuba, north-east of Tokyo, Japan

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Miniatures on display at the Space Dome exhibition hall of the Japan Aerospace Exploration Agency (JAXA) Tsukuba Space Center, in Tsukuba, north-east of Tokyo, Japan. In its facilities, JAXA develop satellites and analyse their observation data, train astronauts for utilization in the Japanese Experiment Module 'Kibo' of the International Space Station (ISS) and develop launch vehicles

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The robot developed by Seed Solutions sings and dances to the music during the Japan Robot Week 2016 at Tokyo Big Sight. At this biennial event, the participating companies exhibit their latest service robotic technologies and components

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The robot developed by Seed Solutions sings and dances to music during the Japan Robot Week 2016 at Tokyo Big Sight

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Government and industry are working together on a robot-like autopilot system that could eliminate the need for a second human pilot in the cockpit

AP

Aurora Flight Sciences' technicians work on an Aircrew Labor In-Cockpit Automantion System (ALIAS) device in the firm's Centaur aircraft at Manassas Airport in Manassas, Va.

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Stefan Schwart and Udo Klingenberg preparing a self-built flight simulator to land at Hong Kong airport, from Rostock, Germany

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Google creates AI that can make its own plans and envisage consequences of its actions - The Independent

A new week brings a fresh Google initiative targeting AI startups. We started the month with the announcement of Gradient Ventures, Googles on-balance sheet AI investment vehicle. Two days later we watched the finalists of Google Clouds machine learning competition pitch to a panel of top AI investors. And today, Googles Launchpad is announcing a new hands-on Studio program to feed hungry AI startups the resources they need to get off the ground and scale.

The thesis is simple not all startups are created the same. AI startups love data and struggle to get enough of it. They often have to go to market in phases, iterating as new data becomes available. And they typically have highly technical teams and a dearth of product talent. You get the picture.

The Launchpad Studio aims to address these needs head-on with specialized data sets, simulation tools and prototyping assistance. Another selling point of the Launchpad Studio is that startups accepted will have access to Google talent, including engineers, IP experts and product specialists.

Launchpad, to date, operates in 40 countries around the world, explains Roy Geva Glasberg, Googles Global Lead for Accelerator efforts. We have worked with over 10,000 startups and trained over 2,000 mentors globally.

This core mentor base will serve as a recruiting pool for mentors that will assist the Studio.Barak Hachamov, board member for Launchpad, has been traveling around the world withGlasberg to identify new mentors for the program.

The idea of a startup studio isnt new. It has been attempted a handful of times in recent years, but seems to have finally caught on withAndy Rubins Playground Global. Playground offers startups extensive services and access to top talent to dial-in products and compete with the largest of tech companies.

On the AI Studio front, Yoshua Bengios Element AI raised a $102 million Series A to create a similar program. Bengio, one of, if not the, most famous AI researchers, can help attract top machine learning talent to enable recruiting parity with top AI groups like Googles DeepMind and Facebooks FAIR. Launchpad Studio wont have Bengio, but it will bringPeter Norvig, Dan Ariely, Yossi Matias and Chris DiBona to the table.

But unlike Playgrounds $300 million accompanying venture capital arm and Elements own coffers, Launchpad Studio doesnt actually have any capital to deploy. On one hand, capital completes the package. On the other, Ive never heard a good AI startup complain about not being able to raise funding.

Launchpad Studio sits on top of the Google Developer Launchpad network. The group has been operating an accelerator with global scale for some time now. Now on its fourth class of startups, the team has had time to flesh out its vision and build relationships with experts within Google to ease startup woes.

Launchpad has positioned itself as the Google global program for startups, asserts Glasberg. It is the most scaleable tool Google has today to reach, empower, train and support startups globally.

With all the resources in the world, Googles biggest challenge with its Studio wont be vision or execution but this doesnt guarantee everything will be smooth sailing. Between GV, Capital G, Gradient Ventures, GCP and Studio, entrepreneurs are going to have a lot of potential touch-points with the company.

On paper, Launchpad Studio is the Switzerland of Googles programs. It doesnt aim to make money or strengthen Google Clouds positioning. But from the perspective of founders, theres bound to be some confusion. In an ideal world we will see a meeting of the minds between Launchpads Glasberg, Gradients Anna Patterson and GCPs Sam OKeefe.

The Launchpad Studio will be based in San Francisco, with additional operations in Tel Aviv and New York City. Eventually Toronto, London, Bangalore and Singapore will host events locally for AI founders.

Applications to the Studio are now open if youre interested you can apply here.The program itself is stage-agnostic, so there are no restrictions on size. Ideally early and later-stage startups can learn from each other as they scale machine learning models to larger audiences.

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Google launches its own AI Studio to foster machine intelligence ... - TechCrunch

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The First AI Bubble Is Now Here: Talking Speakers Wall Street Journal (subscription) Smart speakers powered by artificial intelligence are one of the hottest consumer products in the U.S. Amazon.com Inc., Alphabet Inc.'s Google and Apple Inc. are all offering competing products. If they think that's a crowded field, they should come to ...

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The First AI Bubble Is Now Here: Talking Speakers - Wall Street Journal (subscription)

Data is critical to building great AI so much so, that researchers in the field compare it to coal during the Industrial Revolution. Those that have it will steam ahead. Those that dont will be left in the dust. In the current AI boom, its obvious who has it: tech giants like Google, Facebook, and Baidu.

Thats worrying news. After all, many of these companies have near monopolies in areas like search and social media. Their position helps them gather data, which helps them build better AI, which helps them stay ahead of rivals. For the firms themselves, its a virtuous cycle, but without viable competition, companies can and do abuse their dominance.

Now a new project from the Mozilla Foundation (the nonprofit creator of the Firefox browser) is experimenting with an alternative to data monopolies, by asking users to pool information in order to power open-sourced AI initiatives. The companys first project is called Common Voice, with the Mozilla foundation asking volunteers to donate vocal samples to build an open-source voice recognition system like the ones powering Siri and Alexa.

the power to control speech recognition could end up in just a few hands.

Currently, the power to control speech recognition could end up in just a few hands, and we didnt want to see that, Sean White, vice president of emerging technology at Mozilla, tells The Verge. He says to get data, the big companies can just filter everything coming in, but for other players, there needs to be other methods. The interesting question for us, is, can we do it so the people who are creating the data also benefit? he asks.

At the moment, Mozilla is just collecting data, but plans to have its open-source voice recognition available by the end of the year. (Will it go in the Firefox browser? White wont say, but adds: We have some experiments planned [for that].) Currently, anyone can go to the Common Voice website and donate their voice by reading out sample sentences. They can also supply biographical information like age, location, gender, and accent. This information will help Mozilla avoid bias in creating its voice recognition systems, says White, and ensure that the technology can handle accents something Google and Apple still struggle with.

Frederike Kaltheuner, a researcher at Privacy International, says these firms often use AI as a pretext for scooping up valuable personal data, telling users it will enable them to improve certain services. This may be true, she says, but the consequences of sharing this data for society at large is less clear. There is [often] a fundamental conflict of interest between what you need as a citizen, and what is in that companys interest, says Kaltheuner.

What can open-source data offer that companies cant?

So how does an initiative like Common Voice lure users away from existing and admittedly convenient services? After all, open-source projects have been around for longer than the internet, but with a few exceptions, they have been unable to compete with commercial products. They simply dont offer a comparable service.

For Mozilla, the answer is personalization. After all, while AI systems trained on population-sized datasets tend to be good enough for the average individual, they often fail when it comes to serving the needs of smaller groups, or those not represented in their data. (More often than not, the data is just biased toward white males, the industry default.)

For us to be successful with data commons, there has to be a motivation [for users] other than realizing one day that theyve been giving away all their personal data, says White. We have to make their experience better because theyve participated. In the case of Common Voice, White wants as much accent data as possible to improve voice recognition for these individuals. We want the system to work better for you because some of your data is included, he says.

Offering personalization in exchange for data is a neat proposition, but its not a silver bullet for those fighting data monopolies. For a start, big firms could make similar offers of their own to users. (Alexa doesnt understand you? Read this 10-minute script and well improve its voice recognition.) Or they could spend money to plug the gaps in their own datasets. Google, for example, gets third-party companies to pay Redditors with accents to record their own voice samples.

White acknowledges that the Common Voice project doesnt have an answer to a lot of these questions, but says Mozilla is still dedicated to the core cause of open data. It feels like a true democratizing activity, he says. And there are plenty of organizations that share this ethos. Theres machine learning community Kaggle, which has a large store of user-contributed datasets for AI scientists to play with; the Elon Musk-funded OpenAI, which open-sources all its work; and Healthcare.ai, which publishes free-to-use medical algorithms. And some of these manage to both share open-source data and research while selling their own commercial products, like self-driving car startup Comma.AI.

Although the AI systems we interact with on a daily basis are built on proprietary data, theres a whole world of researchers and institutions publishing useful, if rudimentary, open-source alternatives.

To take these projects to the next level, though, proponents of open-source data may have enlist higher powers to take on the tech giants. Chris Nicholson, CEO of deep learning company Skymind, says, We may need third parties to step in NGOs, governments, coalitions of smaller private firms and pool their data. Nicholson suggests that sharing health care data can improve medical imaging technology, and driver data can make autonomous cars more natural and intuitive on the road. Sharing these types of datasets, he says, has obvious public benefits.

Donating your voice, then, may just be the beginning.

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Mozilla is crowdsourcing voice recognition to make AI work for the people - The Verge

AI is finding its way into every day business and government. The idea of AI is not a new, but what is different is that today's hardware and software is bringing the various concepts underpinning AI to a mass market.

Whats new, too, is the driver: from bots and digital assistants to autonomous vehicles Google, Microsoft, Facebook, Nvidia and others in Silicon Valley are setting a drum beat to which the rest of are marching.

Such is the drumbeat, IDC last year reckoned the AI market would be worth $47bn by 2020, up from $8bn in 2016, with those adopting it fastest in banking, retail, healthcare and discrete manufacturing. Nearly half that spend will go on software.

As business leaders ponder the impact on business models and what capabilities could perform better as a result of injection of AI, their IT managers are finding themselves with a fresh set of concerns: how to assemble a team who can deliver the types of AI be they bots or some kind of neural network that management wants.

Its falling to IT types to identify the skills and people to deliver them to turn their organisations AI vision into a reality.

Where do you start and who can you get? Its tricky, when you consider there are more openings in the AI than people to place them. A Paysa study this year reckoned there were 10,000 open AI positions at the worlds top 20 employers.

Forty per cent are open at companies with more than 10,000 staff, with 10 per cent at those whose employees number 1,001 and upwards.

Roles in demand include number-crunchers - the modern-day equivalent of data analysts; modellers who enjoy analyzing complex data sets; those specialized in deep learning to deal with enormous amounts of data and trying to pull out results, insights and possibilities; and, naturally, engineers to hack the thing together.

When it comes to language, perhaps this is the easiest box to tick.

Increasingly, R and Python tend to be the most commonly used programming languages in this area. Looking at a more hardware-optimized path, going down to the GPU? Then skills in C/C++ could be the ticket.

But data is where things get tricky, which is a challenge as AI is predicated on ML and ML eats data.

Software and service provider Amdocs reckons one answer is to turn to those to your team who already have experience in data and offer some re-training.

This is about retraining BI and data analysts but getting down to the nitty gritty of developing algorithms and that might sit outside of their comfort zone, says Doran Youngerwood, Amdocs head of digital and intelligence. Organisations that have access to fresh data in real time will be most successful. Before you talk intelligence, you need to focus on accessing data and finding complete data sets.

The make-up of teams really depends on the outcomes you are looking to achieve, warns Callum Adamson, founder of API specialist Distributed, which manages distributed AI teams on behalf of clients.

You need to mobilise around the jobs to be done and bring in the roles that allow you to do those jobs. You need to look at the outcomes and break it down, remembering that the best AI is narrow and deep.

Although very hard to find, it is best if the ML experts are also expert coders. Otherwise you may have contention between the algorithm folks and those who have to code it up, warns Hal Lonas, CTO at cybersecurity software company Webroot.

James Waterhouse is head of insight & data science at Sky Betting and Gaming. His team of three data scientists, a test engineer and an intern are tasked with modelling data to better understand churn and cross-sell opportunities.

I dont think theres a perfect data scientist that bridges the skills you need to make things work at scale in real time on a massive platform all while understanding the business. Dont try to find a data scientist unicorn, he warns. Id find three people and get them working together in a way that their skills rub off on each other, Waterhouse told The Register.

That need for collaboration is reiterated by James Poyser, co-founder and managing director of online accountancy software company inniAccounts, which last year won a Queens Award for Innovation for its application of Microsoft AzureML to apply AI to routine compliance tasks related to tax.

Our approach is 'a game of inches - its the sum of a lot of micro services that make a big difference to the user experience, he explains.

As such, Poyser says the combination of technology and collaboration is vital to getting AI projects off the ground successfully. If you approach AI as a technical function alone you wont succeed and youll alienate people and customers, Poyser warns.

The people involved have to evangelise its benefits within the company and educate colleagues so that everyone, no matter their role, can spot an opportunity to apply AI to improve how they work and how customers are served.

AI is an unknown unknown. We don't know what it can do, and we probably don't know where it can be applied. So there is a chain of people and skills that are necessary to getting AI working within a company, Poyser adds. But unless the skills work together you cant create a product that solves a persons problem accurately 90 per cent of the time.

Online retailer Ocado built and installed a system using Googles Tensorflow to do the AI heavy lifting on inbound customer emails at its call centre. The system, built using Python, C++ and Kubernetes and that runs on Google Compute opens and scans up to 2,000 emails on an ordinary day for key words and context, before prioritising and forwarding them. Email numbers double that at busy times such as Christmas.

Ocado spent almost a year building up its Poland-based data science team from scratch. Tim Bickley, team leader in the Ocado Technology data science team, says while a large proportion boast a mathematical background, the flavour of qualifications is less important than strong maths skills, a proven track record of independent research and problem solving, and solid programming skills.

We find the team benefits from having some people who are particularly strong in one area or another, but doesn't work so well if someone is outright weak in one, Bickley said.

AI is not a new field but the demand is meaning skills are in short supply and theres bidding war under way.

In the US, San Francisco at the top of the Silicon Valley is a city where employers are trawling most of AI-related skills. The shortage and the competition is pushing up salaries an average of $157,335 according to Paysa.

Webroots Lonas says: Much of the demand for these skills is coming from very high compensation companies and organisations, so its hard for small companies and startups to compete. My advice is to find one or two experienced experts, use them as the core of the team and then work with local educational institutions to find and fund programs.

Think about using internships, special projects, and growing a farm team. Think about hackathons and other non-traditional ways to find talent. Once you get critical mass, its easier because others will join knowing they can learn from your resident experts and add valuable experience to their resume and careers, Lonas adds.

Sky Betting and Gaming has forged strong relationships with Leeds and Lancaster universities offering students work placements. Waterhouse says this is helping to remove some of the risk from the AI recruitment process. Its useful in getting people in. You can see what theyre good at and it gives them an opportunity to get up to speed with our business.

Academia is a good place to start the hunt for ML experts particularly those with a scientific and engineering background but dont rule out the self-taught. Contributions to ML-related open source projects or published research can be good indicators of technical ability. But prepare to invest in some upskilling, regardless of their background.

Wael Elrifai, senior director of Enterprise Solutions at Pentaho and the companys AI and Machine Learning expert, is currently building a team of more than 20 engineers and data scientists. Having recognised that PhDs or Masters degrees in machine learning are virtually non-existent, Pentaho has turned to training company Pivigo, which specialises in turning PhDs and MScs into Data Scientists and bridging the skills from traditional STEM degree areas to data science, machine learning and AI.

Students have the opportunity during their training to apply what they learn by working on real projects. I recruited my last data scientist through a similar organisation and she is doing really valuable work for the team. She has a PhD in computational fluid dynamics, which has nothing to do with data science. After a four month conversion course, she now has strong practical knowledge in how to solve data science problems, Elrifai says.

Bearing in mind how quickly the ML and AI fields are evolving, a proven ability - and a desire - to quickly learn new technologies is almost more important than pre-existing experience for members of your team. PhDs are desirable but neither necessary nor sufficient; we've had great people without them and the occasional interviewee with them that made us wonder if they found it in a cereal packet, Bickley says.

So technology is the key right? Not quite and heres where things get tricky. If it was a matter of simply finding qualified or aspiring data scientists and associated experts that make the task of building an AI team if not completely simple then at least relatively clear. Paysa found while 35 per cent of the open AI positions in the US required a Ph.D level qualification, 26 per cent needed just a masters degree and 18 per cent a bachelors degree.

But whats akin to gold dust in this hunt is finding people who possess a deep understanding of wider business. It can be easy to get stuck in research-mode for a long time, and forget about the value of your work to the business. Your always need to make conscious decisions based on the data but also the cost/value analysis, says Ocado software development manager Roland Plaszowski.

A killer combination is app developers who understand how AI/ML can give their product the edge, but who also have the ability to effectively collaborate with product managers who are closer to the customer.

That will allow them to apply some intelligence to the usage data, learn about peoples habits and use that insight to develop a product that offers a smoother experience, says inniAccounts Poyser.

So the team is assembled, but its not a thing thats written in stone and the teams composition will evolve.

During the early stages of your project, its likely data will dominate as ML engineers and data scientists will operate a full stack of analysis. Data scraping, cleaning and management can often consume a huge amount of effort.

As the project matures, so the team will grow and more specialized roles emerge, for example, with the addition of data engineers who manage big data infrastructure such as Spark.

You'll also find that the team follows a pattern familiar in traditional IT, particularly software development and DevOps.

"The differences between generic projects and ML/AI projects are not so big. We work hard to make sure that tests, continuous integration, monitoring, automation and documentation are in the project from the beginning, just like any other software engineering project, Plaszowski said.

We'll be covering machine learning, AI and analytics and ethics at MCubed London in October. Full details, including early bird tickets, right here.

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You need to assemble a crack AI team: Where do you even start? - The Register

When it comes to artificial intelligence, Mark Zuckerberg is more of a glass-half-full guy whereas Elon Musk sees the glass as half empty.

Zuckerberg, Facebooks CEO, wrote a post Tuesday evening in which he shared his optimism over the rise of AI technologies like deep learning and how they could lead to breakthroughs in areas like healthcare and self-driving cars.

Normally, this wouldnt be noteworthy, considering its pretty obvious Zuckerberg views the rise of AI through rose-tinted glasses. The CEO has made AI a big priority for his company by hiring one of the pioneers of deep learning, Yann LeCun, as its AI research chief. Zuckerberg also created a special Facebook unit whose mission is to incorporate cutting-edge AI research into its products, and his company regularly releases research papers that highlight progress Facebook is making in AI.

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Given that the Facebook ( fb ) CEO is clearly a believer in AI, why is he going further out of his way to express enthusiasm over the technology, when his company's actions speak loudly enough?

Left unsaid by Zuckerberg were recent comments made by Elon Musk on Tuesday in which the Tesla ( tsla ) and SpaceX ( spacex ) CEO publicly called out Zuckerberg over what Musk believes is the Facebook CEOs limited understanding of AI . Zuckerberg's Tuesday comments also included a reference to a new Facebook AI paper that won an award at a "top computer vision conference," as if to point to Musk that he has more than a "limited" understanding of the tech.

Musks comments came following a recent live Facebook broadcast in which Zuckerberg criticized people who believe that AI will cause doomsday scenarios.

"I think people who are naysayers and try to drum up these doomsday scenarios I just, I don't understand it, Zuckerberg said at the time. It's really negative and in some ways I actually think it is pretty irresponsible."

Zuckerberg comments didn't specifically single out Musk, who recently caused headlines when he told members of the National Governors Association that AI is the greatest risk we face as a civilization. Musk even told the attendees a similar hypothetical situation he shared in a documentary by filmmaker Werner Herzog in which he said AI could potentially lead to wars if used unethically.

"If you were a hedge fund or private equity fund and you said, 'Well, all I want my AI to do is maximize the value of my portfolio,'" Musk said in the documentary, "then the AI could decide, the best way to do that is to short consumer stocks, go long defense stocks, and start a war."

But Zuckerberg doesnt dwell on the bad like Musk does, and by focusing on AIs negative effects, the Facebook CEO believes Musk is doing a disservice in conjuring doom-and-gloom images in peoples minds.

Many other AI experts share Zuckerberg's beliefs, as a recent Wired story on Musks comments indicates. Many of us have tried to educate him and others like him about real vs. imaginary dangers of AI, but apparently none of it has made a dent, Pedro Domingos, a University of Washington machine-learning professor told Wired.

Although Zuckerberg and Musk will likely continue trading barbs over their views on AI, the one thing they can both agree on is that the technology has become fundamental to their respective businesses.

Teslas self-driving cars, for example, wont be able to improve in their capabilities without continued advances in machine learning. Meanwhile, Facebooks various recommendation services are also incorporating AI to better predict what people want to read and watch. Whether it's good or bad that tech giants like Facebook, Google , and even Tesla are hiring some of best AI talent and hoarding people's data to improve their services depends on how you view that glass of water.

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Mark Zuckerberg Argues Against Elon Musk's View of Artificial Intelligence Again - Fortune

One way to develop a reputation as a visionary is to come up with a well-known, startlingly prescient prediction that proves true. Another way is to gain immense wealth and fame through the development of a breakthrough productsay, PayPalor twomaybe Teslaor threeSpaceXand then use your well-funded megaphone to cast prognostications so far and wide and so often that the world comes to simply accept you as someone who sees the future. Even better if you can start a public debate with other famous visionaries, say Facebooks Mark Zuckerberg, Bill Gates, and Stephen Hawking. This is what Elon Musk has just done at the U.S National Governors Association meeting in July 2017.

Elon Musk (BRENDAN SMIALOWSKI)

Musks comments about artificial intelligence (AI) were startling and alarming, beginning with his assertion that robots will do everything better than us. I have exposure to the most cutting-edge A.I., Musk said, and I think people should be really concerned by it.

His vision of the potential conflict is outright frightening: I keep sounding the alarm bell but until people see robots going down the street killing people, they dont know how to react because it seems so ethereal.

Musks pitch to the governors was partly about robots stealing jobs from humans, a concern weve covered on Big Think, and partly a Skynet scenario, with an emphasis on humanitys weak odds of prevailing in the battle on the horizon. His point? A.I. is a rare case where I think we need to be proactive in regulation [rather] than be reactive."

It was this dire tone that caused Facebooks Mark Zuckerberg to take issue with Musks position when asked about it in a Facebook Live chat. "I think people who are naysayers and try to drum up these doomsday scenariosI don't understand it," said Zuckerberg. "It's really negative, and in some ways I think it's pretty irresponsible."

Mark Zuckerberg (JUSTIN SULLIVAN)

As CEO of Facebook, Zuckerberg is as cranium-deep into AI as Musk, but has a totally different take on it. I'm really optimistic. Technology can always be used for good and bad, and you need to be careful about how you build it, and what you build, and how it's going to be used. But people are arguing for slowing down the process of building AII just find that really questionable. I have a hard time wrapping my head around that."

Musk tweeted his response.

Oh, snap.

Hes not the only one discussing this on Twitter. AI experts chimed in to denounce Musks fear-mongering as not being a constructive contribution to the a calm, reasoned discussion of AIs promises and potential hazards.

Pedro Domingos, of the University of Washington, put it most succinctly.

And lets not forget about the imperfect humans who create AI in the first place.

Its not as if Musk is the only one concerned about the long-term dangers of AIits more about his extreme way of talking about it. As Maureen Dowd noted in her March 2013 Vanity Fair piece, Some in Silicon Valley argue that Musk is interested less in saving the world than in buffing his brand, and that he is exploiting a deeply rooted conflict: the one between man and machine, and our fear that the creation will turn against us.

Be that as it may, some are not as sanguine as Zuckerberg about what awaits us down the road with AI.

Stephen Hawking, for one, has warned us to tread carefully before we bestow intelligence on machines, saying, "It would take off on its own, and re-design itself at an ever increasing rate. Humans, who are limited by slow biological evolution, Hawking said, couldn't compete, and would be superseded." Hes also warned, A super intelligent AI will be extremely good at accomplishing its goals, and if those goals aren't aligned with ours, we're in trouble.

We do already know that AI has an odd, non-human way of thinking that even its programmers are having a hard time understanding. Will machines surprise useven horrify uswith decisions no human would ever make?

Bill Gates has alsoexpressed concerns: "I am in the camp that is concerned about super intelligence," Gates wrote during aReddit Ask Me Anything session. "First the machines will do a lot of jobs for us and not be super intelligent. That should be positive if we manage it well. A few decades after that though the intelligence is strong enough to be a concern. I agree with Elon Musk and some others on this and don't understand why some people are not concerned."

Bill Gates (ALEX WONG)

As to how the governors group took Musks warning, theres some evidence to suggest his sheer star power may have overwhelmed some politicians. Colorado Governor John Hickenlooper, for example, told NPR, You could have heard a pin drop. A couple of times he paused and it was totally silent. I felt likeI think a lot of us felt likewe were in the presence of Alexander Graham Bell or Thomas Alva Edison ... because he looks at things in such a different perspective.

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Elon Musk and Mark Zuckerberg Spar Over How Dangerous AI Really Is - Big Think