Daily Archives: July 27, 2017

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."

Follow this link:

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

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)

Read more here:

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

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.

Read more:

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

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

Visit link:

Farmers turn to artificial intelligence to grow better crops - CNNMoney

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.

The rest is here:

Artificial intelligence is infiltrating ad tech - Digiday