Monthly Archives: July 2017

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

Posted: July 27, 2017 at 10:27 am

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

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Mark Zuckerberg Argues Against Elon Musk’s View of Artificial Intelligence Again – Fortune

Posted: at 10:27 am

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

Posted: at 10:27 am

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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The rise of artificial intelligence: What you should and shouldn’t be worried about – Fremont Tribune

Posted: at 10:27 am

SAN FRANCISCO (AP) Tech titans Mark Zuckerberg and Elon Musk recently slugged it out online over the possible threat artificial intelligence might one day pose to the human race, although you could be forgiven if you don't see why this seems like a pressing question.

Thanks to AI, computers are learning to do a variety of tasks that have long eluded them everything from driving cars to detecting cancerous skin lesions to writing news stories. But Musk, the founder of Tesla Motors and SpaceX, worries that AI systems could soon surpass humans, potentially leading to our deliberate (or inadvertent) extinction.

Two weeks ago, Musk warned U.S. governors to get educated and start considering ways to regulate AI in order to ward off the threat. "Once there is awareness, people will be extremely afraid," he said at the time.

Zuckerberg, the founder and CEO of Facebook, took exception. In a Facebook Live feed recorded Saturday in front of his barbecue smoker, Zuckerberg hit back at Musk, saying people who "drum up these doomsday scenarios" are "pretty irresponsible." On Tuesday, Musk slammed back on Twitter, writing that "I've talked to Mark about this. His understanding of the subject is limited."

Here's a look at what's behind this high-tech flare-up and what you should and shouldn't be worried about.

A view of the campus of Dartmouth College, Hanover, New Hampshire, Fall 1966. (AP Photo)

Back in 1956, scholars gathered at Dartmouth College to begin considering how to build computers that could improve themselves and take on problems that only humans could handle. That's still a workable definition of artificial intelligence.

An initial burst of enthusiasm at the time, however, devolved into an "AI winter" lasting many decades as early efforts largely failed to create machines that could think and learn or even listen, see or speak.

That started changing five years ago. In 2012, a team led by Geoffrey Hinton at the University of Toronto proved that a system using a brain-like neural network could "learn" to recognize images. That same year, a team at Google led by Andrew Ng taught a computer system to recognize cats in YouTube videos without ever being taught what a cat was.

Since then, computers have made enormous strides in vision, speech and complex game analysis. One AI system recently beat the world's top player of the ancient board game Go.

South Korean professional Go player Lee Sedol, right, watches as Google DeepMind's lead programmer Aja Huang, left, puts the Google's artificial intelligence program, AlphaGo's first stone during the final match of the Google DeepMind Challenge Match in Seoul, South Korea, Tuesday, March 15, 2016. A champion Go player scored his first win over a Go-playing computer program on Sunday after losing three straight times in the ancient Chinese board game, saying he finally found weaknesses in the software. (AP Photo/Lee Jin-man)

For a computer to become a "general purpose" AI system, it would need to do more than just one simple task like drive, pick up objects, or predict crop yields. Those are the sorts of tasks to which AI systems are largely limited today.

But they might not be hobbled for too long. According to Stuart Russell, a computer scientist at the University of California at Berkeley, AI systems may reach a turning point when they gain the ability to understand language at the level of a college student. That, he said, is "pretty likely to happen within the next decade."

While that on its own won't produce a robot overlord, it does mean that AI systems could read "everything the human race has ever written in every language," Russell said. That alone would provide them with far more knowledge than any individual human.

The question then is what happens next. One set of futurists believe that such machines could continue learning and expanding their power at an exponential rate, far outstripping humanity in short order. Some dub that potential event a "singularity," a term connoting change far beyond the ability of humans to grasp.

The Waymo driverless car is displayed during a Google event, Tuesday, Dec. 13, 2016, in San Francisco. The self-driving car project that Google started seven years ago has grown into a company called Waymo. The new identity announced Tuesday marks another step in an effort to revolutionize the way people get around. Instead of driving themselves, people will be chauffeured in robot-controlled vehicles if Waymo, automakers and ride-hailing service Uber realize their vision within the next few years. (AP Photo/Eric Risberg)

No one knows if the singularity is simply science fiction or not. In the meantime, however, the rise of AI offers plenty of other issues to deal with.

AI-driven automation is leading to a resurgence of U.S. manufacturing but not manufacturing jobs . Self-driving vehicles being tested now could ultimately displace many of the almost 4 million professional truck, bus and cab drivers now working in the U.S.

Human biases can also creep into AI systems. A chatbot released by Microsoft called Tay began tweeting offensive and racist remarks after online trolls baited it with what the company called "inappropriate" comments.

Harvard University professor Latanya Sweeney found that searching in Google for names associated with black people more often brought up ads suggesting a criminal arrest. Examples of image-recognition bias abound.

"AI is being created by a very elite few, and they have a particular way of thinking that's not necessarily reflective of society as a whole," says Mariya Yao, chief technology officer of AI consultancy TopBots.

Tesla and SpaceX CEO Elon Musk bows as he shakes hands with Republican Nevada Gov. Brian Sandoval after Musk spoke at the closing plenary session entitled "Introducing the New Chairs Initiative - Ahead" on the third day of the National Governors Association's meeting Saturday, July 15, 2017, in Providence, R.I. (AP Photo/Stephan Savoia)

In his speech to the governors, Musk urged governors to be proactive, rather than reactive, in regulating AI, although he didn't offer many specifics. And when a conservative Republican governor challenged him on the value of regulation, Musk retreated and said he was mostly asking for government to gain more "insight" into potential issues presented by AI.

Of course, the prosaic use of AI will almost certainly challenge existing legal norms and regulations. When a self-driving car causes a fatal accident, or an AI-driven medical system provides an incorrect medical diagnosis, society will need rules in place for determining legal responsibility and liability.

With such immediate challenges ahead, worrying about superintelligent computers "would be a tragic waste of time," said Andrew Moore, dean of the computer science school at Carnegie Mellon University.

That's because machines aren't now capable of thinking out of the box in ways they weren't programmed for, he said. "That is something which no one in the field of AI has got any idea about."

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Google for help if you want a hand on artificial intelligence, machine learning – Economic Times

Posted: at 10:27 am

SAN FRANCISCO: Machine learning and AI-based startups can Google for help as the search giant launches its Google Developers Launchpad Studio Accelerator Programme for startups to build and scale their products across the globe.

The accelerator programme is targeting startups in all global markets, including India, as well as homegrown players in the US. The length of the programme is still being worked out.

"In the past four years (of Google Launchpad Accelerator), we have learned a lot while supporting early and late-stage founders," said Roy Glasberg, the global lead at Google Developers Launchpad.

"While working with startups on innovative solutions, such as applying artificial intelligence to solve transportation problems in Israel, improving tele-medicine in Brazil and optimising online retail in India, we have learned that these firms require specialised services," Glasberg said.

The startups selected for the studio programme will have access to applied artificial intelligence integration toolkits, product validation support which includes use-case workshops with Fortune 500 industry practitioners and artificial intelligence experts at Google as well as venture capital investors in Google, Silicon Valley and other global hotspots.

Google Developers Launchpad Studio has tailored technical, product and investment solutions for artificial intelligence and machine learning startups across stages, from early-stage to late-stage players. "Whether you are a three person team or an established post series-B startup trying to apply AI and machine learning to the product offering, Google is interested talking to you," Glasberg said.

Launchpad Studio will be head quartered in San Francisco at Launchpad Space, with hubs in Tel Aviv and New York. Google also has plans to expand operations to Bengaluru, Toronto, London and Singapore.

"Innovation is open to everyone, worldwide. With this global programme, we now have an important opportunity to support entrepreneurs everywhere who are planning to use AI to solve for the biggest challenges," said Yossi Matias, VP of Engineering at Google.

(The reporter was in San Francisco at the invitation of Google)

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The Role of Artificial Intelligence in Intellectual Property – IPWatchdog.com

Posted: at 10:27 am

Artificial Intelligence (AI) has been a technology with promise for decades. The ability to manipulate huge volumes of data quickly and efficiently, identifying patterns and quickly analyzing the most optimal solution can be applied to thousands of day-to-day scenarios. However, it is set to come of age in the era of big data and real time decisions where AI can provide solutions to age old issues and challenges.

Consider, as an example, traffic management. The first traffic management system in London was a manually operated gas-lit traffic signal, which promptly exploded two months after its introduction. Since this inauspicious start, a complex network of road closures, traffic management systems, traffic lights and pedestrian crossings have served to drive increased complexity into travelling in the City. Today traffic travels slower than ever, despite the plethora of new systems being added to better manage the system.

AI has the potential to change this. It can harvest data on traffic volumes, historical trends and current blockages to quickly calculate the most optimal solution for traffic in London. It can do this in near real time, constantly tweaking and managing flow to deliver the best possible solution.

This is why AI is increasingly the go to technology for organisations wanting to solve highly complex and data heavy challenges. Digital retailers are using AI-powered robots to run warehouses. Utilities are using AI to forecast electricity demand. Mobile networks are deploying AI to manage an ever-increasing demand for data. We stand on the threshold of a new age of AI powered technology.

The Intellectual Property (IP) industry is another market where AI could have a profound effect. Traditionally powered by paper, manual searches and lengthy decision-making processes, AI can be deployed to simplify day-to-day tasks and deliver increased insight from IP data.

IP administrative tasks are one of the most time intensive and risky areas of IP. Law firms and corporate IP departments may, at any time, cover thousands of individual items of IP data, across hundreds of jurisdictions, dealing with thousands of different products. Historically this has been a significantly manual and slow process.

Consider one single patent that a company has applied for protection for in many different countries. A network of agents, familiar with the specific processes required to gain protection in specific countries, will each help the company achieve their goal. Along the way, hundreds of items of paperwork will be generated, in multiple languages, each with their own challenges and opportunities.

All of this information would currently be assessed manually and then input into an IP management system. Naturally enough this could easily result in many data processing errors. Now consider this across multiple patents. The opportunities for error are almost limitless. Yet for many companies IP remains its most valuable asset. A simple error in inputting a renewal date could risk losing an asset worth millions to a company. It is worth noting that the World Intellectual Property Organisation (WIPO) estimates around a quarter of patent information is wrong. The risks are therefore very evident.

In addition, considerable time and cost accrues from the manual labour involved in inputting data. This is activity that, if it can be automated, frees law firms and IP experts to focus on more strategic issues. AI, which is highly adept at processing large sets of data quickly and accurately, can help both efficiency and accuracy. This also enables law firms and IP professionals to take on a more strategic role within the organisation, generating insight from data to help shape future company performance, whilst leaving the more mundane aspects of IP management to computers.

By automating the submission of data and ensuring that every single item of IP has a unique identifier, correspondence from the various patent offices and agent networks can be simply sorted and searchable on demand. An AI engine can then be deployed to identify relevant information in correspondence, resulting in faster and more accurate outcomes.

The number of IP assets globally is growing. According to the WIPO there was a 7.8% growth in patent filings between 2014 and 2015. This upward trend in filings has continued for at least 20 years. Therefore, IP documentation and resources are growing. Finding relevant information in this vast amount of data is becoming more difficult. Historically, searches have been carried out manually, with static search databases being the only support tools.

AI and Machine Learning (ML) can not only automate the process of searching huge databases but also store and use previously collected data to improve the accuracy of future searches. AI can also be used to provide insight into a geographical or vertical market. Consider a company looking to exploit IP in new regions. It may wish to consider the best countries to file for protection. Insight into the strengths and weaknesses of markets in certain countries could be cross referenced with competitive IP data to deliver an instant overview of the most beneficial geographies to apply for further protection. Research that would have previously taken months to achieve can be managed in minutes by deploying AI in an effective way.

A large IP portfolio is bound to have both strengths and weakness. Indeed, one of the weaknesses may be the sheer scope of the portfolio. As a patent portfolio increases in size, it becomes difficult to effectively oversee and draw insight from the portfolio. As a result, firms are not only limited in managing processes such as renewals, but also in using insight to gain a competitive advantage.

Many IP professionals are already analysing the value of their patent portfolio. Which patents are most effective? Which deliver most licencing revenues? In which countries? What is the value of IP to a business compared to the cost of renewal? By analysing large sets of data, AI is able to indicate where a companys portfolio of IP is strongest and weakest.

This can, in turn shape future investment decisions in research and development, help companies understand their relative strengths and weaknesses in terms of their competitors and enable companies to understand more about the potential opportunities in new markets.

AI is now delivering real value to companies that need to solve complex issues. Within IP management, AI can empower IP professionals. Day-to-day IP tasks can be time consuming, but AI technology enables professionals the time to focus on more strategic decisions in their portfolio. It will also drive improved accuracy while reducing the risk of IP insight and intelligence moving on as employees do. For IP professionals, the real opportunity however comes from the insight that AI can provide into otherwise impenetrable and inaccessible volumes of data. AI will help IP professionals generate business insight that can open up new markets, accurately value an IP portfolio and deliver a better understanding of what and where the next generation of IP investment should come from.

Tyron Stading is the Chief Data Officer for CPA Global, where he is responsible for creating unified data integration and analytics across all of our products and services. In 2006, Tyron founded and served as CTO for Innography, the US-based IP analytics software provider that CPA Global acquired in 2015. He was previously employed at IBM and several other high technology start-ups. Tyron earned a Computer Science degree from Stanford University and an MBA from University of Texas at Austin. Tyron has published multiple research papers on intellectual property and personally filed more than 50 patents.

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Farmers turn to artificial intelligence to grow better crops – CNNMoney

Posted: at 10:27 am

NatureSweet, which grows tomatoes on six farms in the United States and Mexico, is using artificial intelligence to better control pests and diseases in its greenhouses.

The technology, developed by the Israeli digital farming company Prospera, has already improved harvests and reduced labor costs. NatureSweet began testing the technology almost a year ago at one of its farms in Arizona. It plans to roll the tech out to all of its locations soon.

Adrian Almeida, chief innovation officer at NatureSweet, believes artificial intelligence will eventually improve his greenhouses tomato yields by 20%.

Related: How farmers use digital agriculture to grow more crops

"It'll be better for the environment and for the customer," Almeida said.

Farms are increasingly using technology to grow crops, from task-tracking systems that monitor watering and seeding to drones that capture aerial images.

So far, NatureSweet's weekly harvests have grown 2 to 4%. This may seem modest, but the results makes a big difference when growing millions of pounds of tomatoes a year.

To use the method, NatureSweet installed 10 cameras in greenhouse ceilings. The cameras continuously take photos of the crops below. Prospera's software has been trained to recognize trouble, such as insect infestations or dying plants.

Previously, some of NatureSweet's 8,000 employees were tasked with walking through the greenhouses to identify struggling plants. But the process was slow and expensive. NatureSweet did this only once a week.

The cameras from Prospera monitor the plants 24/7 and provide instant feedback.

Prospera's founder Daniel Koppel previously researched how to predict crop yields from satellite photos -- insights that can be used to trade commodities on Wall Street. Instead, he built his own business, figuring it would have a greater global impact.

NatureSweet has also experimented with using the cameras to forecast when plants are ready to be harvested.

Although Almeida said that aspect of the technology is still a work in progress, improved efficiency is apparent. He estimated NatureSweet's headcount would have to grow by 4% without it.

The company announced this week it raised $15 million from investors such as Qualcomm Ventures and Cisco Investments to fund expansion. Prospera plans to track more crops, including peppers and potatoes, as well as monitor plants outside greenhouses.

CNNMoney (Washington) First published July 26, 2017: 8:14 AM ET

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Artificial intelligence is infiltrating ad tech – Digiday

Posted: at 10:27 am

Ad tech has AI fever.

Programmatic platforms like Rocket Fuel and Huddled Masses are increasing their use of AI and machine learning to determine which impressions theyre unlikely to win and should avoid bidding on to reduce their infrastructure costs. Last week, Rubicon Project agreed to pay nearly $40 million to acquire nToggle to solve this very problem. Media agencies like Maxus are also using AI to rearrange their data more efficiently. And publishers like CafeMedia use AI to tag and organize their inventory. But despite AIs growing popularity, its usage in advertising remains confined to niche areas.

There is definitely more smoke than fire in the marketplace right now, said Rick Greenberg, CEO of ad agency Kepler Group, which built a platform with AI toolsthatconsolidates reporting across various types of data vendors. But I do believe AI is starting to be used in useful, but limited, ways.

Liane Nadeau, vp of programmatic media at ad agency DigitasLBi, said a practical use of AI for ad buyers is using it to change the ad units shown to targeted users in real time, which is a technology that companies like Sizmek and Xaxis have invested in. Just as targeting helps advertisers reach the right person, dynamic creative platforms use AI to gather data about the site the user is on to ensure the ad unit aligns with not just the users demographics but also the website the user is visiting.

For AI products to work, they need to be tailored to the clients specific use, which is advice that often goes unheeded in sales pitches where third-party AI vendors claim they can solve clients problems themselves. CafeMedia, for example, had to add its own code on top of the IBM Watson platform to get the AI to properly tag its content.

IBM is clear that its a platform, and you really should train it and make it understand your data set, said CafeMedia co-founder Paul Bannister. But other vendors claim their system will work out of the box, and thats where they fall down.

Another limit of AI is that products are weak at preventing ad fraud, said ad fraud researcher Augustine Fou. Although the tech behind AI products might be advanced, the AI still looks for standard fraud patterns, which fraudsters can easily circumvent, he said.

While AI has specific applications in advertising, its worth noting that its often an empty catchphrase marketing departments use to get peoples attention. Three ad buyers told Digiday they never had a single client ask them about AI. Many brand clients are just now beginning to grasp programmatic advertising, so AI isnt of immediate importance to them.

David Lee, programmatic lead at ad agency The Richards Group, said he regularly gets pitches for AI-enabled products but the AI part of the products usually doesnt seem to affect performance outside of being a buzzword.

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Goop promoted her as one of ‘our doctors.’ But Dr. Aviva Romm is concerned the site is becoming a caricature – STAT

Posted: at 10:24 am

T

he headline on Gwyneth Paltrows wellness site, Goop, looked straightforward enough: Uncensored: A word from our doctors.

It featured a defense of the alternative medical practices that Goop has promoted, such as tucking a jade egg in the vagina to enhance sexual pleasure. An attack on an OB-GYN who has publicly slammed Goops advice. And then, open letters from two doctors who have written for Goop in the past.

But one of those physicians, Dr. Aviva Romm, told STAT that she doesnt see herself as Goops doctor at all. She hasnt read most of the content on the site (which promotes things like goats milk cleanses, energy healing stickers, and brain dust to align you with the mighty cosmic flow). She cant give it a scientific stamp of approval. And shes wary of anyone who automatically endorses products or therapies simply because theyre branded as natural.

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In fact, she said shes advised Goop that if it wants to be more than a caricature of everything alternative health for women, the editors need to do an audit of all their content, in consultation with physicians.

I dont think everything in there is necessarily evidence-based or effective, said Romm, who lives in Massachusetts and runs a small practice in New York City.

She added: Im not one of these integrative doctors who basically just because its alternative thinks its safe and good. I try to keep my doctor thinking cap on as well.

Goop said its considering a medical advisory board but hasnt yet established one and in the meantime, uses a number of physicians as sounding boards before publishing its articles. There may be more open letters in the future, a spokeswoman said.

Despite her reluctance to endorse the publication, Romm isnt disavowing Goop.

She has been interested in alternative medicine since her college days, spent 20 years as a midwife and herbalist before getting her M.D. at Yale Medical School, and said she understands why women are dissatisfied with conventional medicine and searching for new paths to well-being.

And she promotes her own takes on alternative medicine some of which have drawn sharp criticism from mainstream doctors.

Romm sells proprietary blends of nutritional supplements branded with her name and sold in formulations such as soothe, nourish, and uplift. She also urges women to consider seasonal detoxes, use herbal alternatives to antibiotics for some infections, and try her month-long program to revamp their adrenal and thyroid health, and in turn, boost energy and lose weight. Critics have saidsome of those ideas arent backed by evidence, either.

Here are excerpts from STATs recent conversation with Romm, condensed and edited for clarity. Some themes touched on more than once in the interview have been consolidated for clarity.

As she explored alternative medicine in college, Romm said her outlook shifted from being the spelling bee, science fair kid to being a do-it-yourselfer hippie.

More recently, she said, I wrote the seminal its always an odd word to put to womens things womens health and botanical medicine textbook.

Thats how she got to Goop. The publicist for her new book suggested she expand her audience by writing for publications including Goop, and put her in touch with someone at the site.

My role with Goop is nothing formal at all, Romm said. I really just write my articles.

The editors at Goop write her from time to time, looking for an article about endometriosis or polycystic ovary syndrome, or a fresh take on Epstein-Barr virus. (Goops first story on the virus was written by a self-proclaimed medical medium who claims to have been guided, at age 4, by a voice to diagnose his grandmothers lung cancer. Romms own take on Epstein-Barr virus that it can cause autoimmunne diseases such as thyroiditis and can be treated with herbal supplements such as lemon balm, licorice, and holy basil has also been criticized by some in the medical community as lacking in evidence.)

Romm isnt paid for her contributions to Goop, nor does she consider herself one of Goops doctors. She said she simply doesnt pay enough attention to Goops content to make a judgment on it.

In short: no.

I think theres this sense that sort of by default by writing for them, I was endorsing them, she said. But Romm said she sees that as the equivalent of assuming that every writer in the New York Times agrees with every piece published in the New York Times.

I had a letter to the editor published in the [New England Journal of Medicine]. I certainly dont endorse everything in NEJM, she said.

Romm got roped into the Goop fight after Dr. Jen Gunter a longtime critic of the site lambasted the lack of scientific evidence behind Goops recommendations in a widely shared post on her blog in May.

When the Goop hit the fan, lets say, with the Jen Gunter piece, it was just kind of in the early stages of my writing for them, she said.

Goop asked her to submit a quote addressing the criticism. She responded that she couldnt endorse the site, but she could share her thoughts on womens wellness. Thats how she came to write the open letter which Goop later published as A word from our doctors.

Romms key goal with that letter: pushing back against a conventional wisdom that she said trivializes women seeking alternative medical options as participating in a wellness trend.

Romm acknowledged that some women may be choosing things that arent necessarily the healthiest, best, or wisest therapies, like constantly detoxing but said thats no justification for dismissing the entire arena of womens alternative medicine in one fell swoop.

But, Romm said, two wrongs dont make a right. Just because women are searching for alternatives to conventional medicine doesnt mean any alternative is a good one.

And she criticized the sea of internet noise and people wearing white coats when theyre not even doctors as confusing women about whats valid, whats trustworthy and whats not.

I cant endorse Goop, in that just because [products are] natural or organic, doesnt mean that theyre beneficial for women, she said. Just because it hasnt been proven harmful and its natural doesnt mean its safe. We cant just say that thats sort of the default position.

You cant just say its better than conventional medicine. If its wrong, its wrong.

Romm said shed start by trying to understand why a patient felt like she needed to jump on a health trend train. Maybe its that shes newly single, feels bad about her body, and wants to lose weight, Romm said. Or perhaps, its that she has migraines and read online that a detox might help.

She might be being told by a rheumatologist that she needs an immunosuppressant drug but maybe theres not great evidence for that either, and shed rather try something more benign for 21 days before she goes on that, she said.

Im really respectful of other peoples choice and autonomy if theres nothing harmful in the plan. Ill say, Great, awesome, give it a try. But not if theres something harmful in the plan, or even if its not harmful but its gonna cost a lot of money out of pocket, she said.

All health care is for wealthy white women, Romm said.

When you look at the statistics on maternal mortality, infant mortality, mental health problems, abuse at home, drug problems, with the exception of the growing opioid problems, which are typically more in the white community, all of these have to do with lack of access to health care which correlates with socioeconomic status, she said.

Romm added that she understands that Goop is certainly commercial.

She is, too, she said: I have to make a living, too. I sell my books and courses on my website.

I think Gwyneth Paltrow was a fabulous actress in her day of acting, and Im not a sort of advocate or antagonist of her work. I understand that she is probably a very decent person, trying to do good work, and [she] does things that feel meaningful to her. And, yes, theres a commercial aspect to it, [but] theres nothing that doesnt have a commercial aspect to it, unless youre a saint doing medical work.

But, Romm said, its not just celebrities and alternative medicine providers who are making money off patients. She pointed to the billions drug companies spend on TV ads.

Lets not be misled here, she said. Those drug company commercials are making lots of people millions. So its not just one isolated situation with Goop.

Reporter, Morning Rounds Writer

Megan writes the Morning Rounds newsletter and covers health and medicine.

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Goop promoted her as one of 'our doctors.' But Dr. Aviva Romm is concerned the site is becoming a caricature - STAT

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Pyrrolizidine alkaloids in tea, herbal infusions and food supplements – EU News

Posted: at 10:24 am

Exposure to pyrrolizidine alkaloids in food, in particular for frequent and high consumers of tea and herbal infusions, is a possible long-term concern for human health due to their potential carcinogenicity, say EFSAs experts.

The consumption of food supplements based on pyrrolizidine alkaloid-producing plants could also result in exposure levels causing short-term toxicity resulting in adverse health effects.

EFSA has updated its 2011 advice on the risks for human and animal health from pyrrolizidine alkaloids, a large group of toxins produced by different plant species that can unintentionally enter the food chain.

The European Commission requested the updated risk assessment, which takes account of exposure estimates using more recent data on the levels of these toxins in honey, tea, herbal infusions and food supplements.

In 2011 EFSA concluded there were possible long-term health concerns for toddlers and children who are high consumers of honey, the only food category for which sufficient data were then available.

EFSAs experts identified 17 pyrrolizidine alkaloids in food and feed that should continue to be monitored and recommended further studies on the toxicity and carcinogenicity of those most commonly found in food.

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