Category Archives: Artificial Intelligence

Purdue University

By Matthew HutsonJul. 27, 2017 , 4:15 PM

People have no trouble looking at a photo and understanding the 3D shapes of the objects withinpeople, cars, Shiba Inus. But computers, with little experience in the real world, arent so smartyet. Now, scientists have created a new unwrapping method that comes much closer. They started by teaching an algorithm to treat 3D objects as 2D surfaces. Imagine, for example, hollowing out a mountainous globe and flattening it into a rectangular map, with each point on the surface displaying latitude, longitude, and altitude. After much practice, the new machine-learning algorithm learned to translate photos of 3D objects (like the first row of planes, above) into 2D surfaces, which can then be stitched into 3D forms. Researchers trained it to reconstruct cars, airplanes, and hands in almost any posture. Whereas an earlier method warped sedans into hatchbacks and rendered planes birdlike (see the second row of airplanes, above), this new method could more accurately infer 3D shapes from photos, the authors reported this week at the Institute of Electrical and Electronics Engineers Conference on Computer Vision and Pattern Recognition, in Honolulu. The new program, called SurfNet (after the word surface), could also invent brand new, realistic-looking 3D shapes for cars, planes, and hands. Future applications might include designing objects for virtual and augmented reality, creating 3D maps of rooms for robot navigation, and designing computer interfaces controlled with hand gestures. Thumbs up.

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Artificial intelligence can turn 2D photos into real-world objects - Science Magazine

In an earlier article on Augmented Reality, I noted that Apple (NASDAQ:AAPL) faces challenges for growth of its iPhone business, as many worldwide markets have become saturated, and the replacement rate for existing customers has dropped. I noted that Apple has weathered this change by continuing to charge premium prices for its product (against the predictions of many naysayers), and it can do this for two reasons.

1- Its design and build quality is unsurpassed, and

2- Its always on the cutting edge of new technology.

For these reasons, customers feel that there is value in the iconic product.

Number two leads the investor to the question:

While the earlier articles centered on augmented reality this will focus on Artificial Intelligence (AI), and Machine Learning (ML), this is an important topic for the investor as it is a critical part of the answer to the question above.

Most analysts focus on the easily visible aspects of devices, ignoring the deeper innovations because they dont understand them. For example, when Apple stunned the tech world in 2013 by introducing the first 64-bit mobile system on a chip (processor), the A7, many pundits played down the importance of the move. They argued that it made little difference, and listed a variety of reasons. Yet they ignored the real important advantages particularly the tremendously strengthened encryption features. This paved the way for the enhanced security features that include complete on-device data encryption, Touch ID and Apple Pay.

Apples foray into AR and now ML are further examples of this. While AR captures the imagination of many people and the new interface has been covered, the less understood Machine Learning interface has been virtually ignored in spite of the fact that going forward it will be a very important enabling technology. Product differentiation and performance are key to Apple maintaining its position, and thus key to the investor's understanding.

Machine Learning is a type of program that gives a response to input without having been explicitly programmed with the knowledge. Instead, it is trained by being presented with a set of inputs and the desired response. From these, the program learns to judge a new input.

This is different from earlier Knowledge Based Systems. These were explicitly programmed. For example, in a simple wine program I developed for a class, there were a long list of rules, essentially of the form:

- IF (type = RED) AND (acidity = LOW) THEN respond with XXX

- IF (type = RED) AND (acidity = HIGH) THEN respond with ZZZ

In a ML system, these rules do not exist. Instead a set of samples are presented and the system learns how to infer the correct responses.

There are a lot of different configurations for such learning systems, many using the Neural Network concept. This is based on the interconnected network of the brain. Here each individual neuron (brain cell) receives a connection from many other neurons, and then in turn connects to many others. As a person experiences new things, the connections between the excited cells get strengthened or facilitated so that a given network is more easily excited in the future if the same or similar input is given.

Computer neural nets work analogously, though obviously digitally. The program defines as set of cells into some series of levels. Each is influenced by some subset of the others and in turn influence yet other cells, until a final level produces a result. The degree to which the value of one cell changes the value of another cell to which it is connected is specified by the weight of the connection. This is where the magic lies.

During training, when a pattern is presented to it, the strong connections are strengthened (and others possibly weakened). This is repeated for various inputs. Eventually, the system is deemed trained, and the set of connections is saved as a trained model for use in an application. (Some systems allow for continued training after deployment.)

(For an interesting anecdote on how this works in the brain, see this story.)

Many people think of AI as some big thing on mainframes such as Watson by IBM (IBM), which championed at Jeopardy, or in research labs at Google (GOOG) (NASDAQ:GOOGL) or Microsoft (MSFT). They think that this is for the big problems of industry.

Research at Google is at the forefront of innovation in Machine Intelligence, with active research exploring virtually all aspects of machine learning, including deep learning and more classical algorithms. Exploring theory as well as application, much of our work on language, speech, translation, visual processing, ranking and prediction relies on Machine Intelligence. In all of those tasks and many others, we gather large volumes of direct or indirect evidence of relationships of interest, applying learning algorithms to understand and generalize. (Google page)

But this is not the case. ML applications are running on your smartphone and home computer now. Text prediction on your keyboard, facial recognition in your photos be it in your photos app or in Facebook (FB) and speech recognition such as Siri, Amazons (AMZN) Echo, etc., all use ML systems to perform the tasks. Many of these are actually sent off to servers in the cloud to do the heavy lifting computing, because it is indeed heavy lifting that is, it requires a great deal of compute power. NVidia (NVDA) is surging precisely because of its new Tesla (NASDAQ:TSLA) series products on the server end of this industry.

So, what has Apple done?

A few weeks ago, Apple (AAPL) held its Developers Conference (WWDC) opening with the keynote address where Tim Cook and friends introduced new features of their line of products. While many focused on the iPad Pro, the new iOS and Mac OS features or the HomePod speaker, for the long term, the real news for the investor is the AR and ML toolkits introduced.

Investors may be wondering:

What Core ML does is simple, it allows app writers to incorporate an ML model into their app by simply dragging it into the program code window. It also provides a single, simple method to send target data into that model and retrieve an answer.

The purpose of a model is to categorize or provide some other simple answer to a set of data. Input might be one piece of data, such as an image, or several, as a stream of words.

The model is a different story altogether. This is the complicated part.

Apple provides access to a lot of standard models. The programmer can simply select one of these, and plop it into the program. If not, then the programmer, or an AI specialist, would go to one of a number of available ML tools to specify a network and train it. Apple has provided tools to translate these trained models into the format that the Core ML process uses. (Apple has provided its format as open source for other developers to use.)

The amazing thing is that one can pull a model into their program code, and then write as little as three or four lines of new code to use it. That is, once you have the model, you can create a simple app to use it literally in a matter of minutes. This is an dazzling accomplishment.

An interesting thing is that the programmers call to the model to send in data and retrieve the response is exactly the same no matter what the model. Obviously one needs to send in the correct type of data (image, sound file, text), but the manner of doing so is exactly the same no matter what type of data is assessed or what the inherent structure is of the model itself. This enormously simplifies programming. The presenters continually emphasized that the developers should focus on the user experience, not on implementation details.

One of the great things about Core ML is that the apps perform all the calculations, on the device. Nothing is sent to a remote server. This provides the following benefits:

One area of interest (at least for the technophile) is some of the benefits of the actual implementation.

Software on a computer (and a smartphone is a computer) is layered, where each layer creates a logical view of the world, but really is no more than a bunch of code using the layer below it. Thus, a developer can call a routine to create a window (sending in a variety of parameters for the size and location, color, etc.), and this will perform the enormous number of operations from the lower levels that are required to open up a graphic display that we recognize as a window. In some cases, the upper layers of abstraction are the same for different devices, in spite of very different real implementations.

The illustration shows Apples implementation of Core ML and how it sits on top of other layers. In this case, there are ML layers for vision, etc. that sit on top of the Core ML itself. But the important thing here is that we can see how Core ML sits on top of Accelerate and Metal Performance Shaders.

Metal is the Apple graphics interface for accelerating graphics performance. It improves this immensely. Shaders are the units that actually perform the calculations in a Graphics Processing Unit (see GPU section of this post).

One might wonder why ML services would be built on top of graphics processors. As noted in the post on GPUs mentioned above, a graphic (photo, drawing, video frame) consists of thousands or millions of tiny picture elements, or pixels. Editing the frame consists of applying some mathematical operation on each of the pixels sometimes depending on its neighbors. This means you want to perform the same operation on millions of different data pieces. As I noted earlier, a neural network consists of many cells each with many connections. One system boasts 650K neurons with 630M connections. Yet the actual adjustments of the weights of the connections is a simple arithmetic operation. So a GPU is actually spectacular at ML processing performing the same calculation on hundreds, or even thousands of cells in parallel. Apples Metal technology lets the ML programs access the GPU compute cells directly.

The important thing to understand here is that Apple has built the Core ML engines on top of these high performance technologies. Thus, it comes for free to the app developer. All the hard work of programming an ML engine has been done, fine tuned, accelerated, and debugged. The importance of this is really hard to convey to the person who does not know the development process. It gives every app developer the benefit of literally scores of programmers working for several years to make their little app, effective, correct, and robust.

Finally, there is one last card in apples hand, yet to be officially shown. Back in May, Bloomberg reported that they had reliable sources tell them that Apple is working on a dedicated ML chip, called the Neural Engine.

This makes a lot of sense. A standard GPU is great for doing ML computations, but in the end, it was designed first to handle graphics. The design would probably be quite similar, but totally tailored to the ML tasks. My guess is that this Neural Engine will make its debut on the iPhone 8 that is expected to be released in the fall (along with updated iPhone 7s/Plus). It would be a tantalizing incentive for buyers, a major differentiator for the line. With time, it would become available on all new phones (perhaps not the low end SE). With this chip, I believe Siri would move completely onto the device. It could also be used on Macs.

ML models require a tremendous amount of computation. As such, they consume a great deal of battery power. As new generations of chips have emerged with continually shrinking transistor size (thus increasing compute power and efficiency), it has become more realistic to run some models locally. Additionally, the GPUs that Apple has built on their A-series chips have grown at an extraordinary rate. Graphics performance in the new iPad Pro, with A10x processor, is an astounding 500 times that of the original iPad. According to Ash Hewson of Serif software, the performance is literally four times that of an Intel i7 quad core desktop PC.

Still, on a portable device, every drop of battery power is precious. So if Apple can save by designing its own specialty chips, then it will be worth it. They have the talent and the capacity.

And yet another motivation. There is still a lot of evidence that Apple is working on self driving car technology. It would be just like them to want to own the process from hardware to software. With their own ML processor, they would be free from worries that some other company would have control of a key technology. (This is why they created the browser Safari.) Metal is a software/hardware interface specification. It relies implicitly on a hardware platform that conforms to its specifications. Having their own Neural Engine chip will assure this, even as they move into self-driving cars.

As an aside it is interesting to note that the Core ML libraries (including Metal 2) will run on the Mac as well as iOS. Apple is gradually moving to unify the two platforms in many respects.

With the iPhone itself, one can try to predict sales and costs and come up with a guess as to revenue and profit for a given time frame. Both ML and AR projects have little in terms of applications at the moment, and so their impact on sales is rather ephemeral at this time. Still, this is an important investment in the future. I stated above that Core ML is an important enabling technology. The fact is simple with a huge lead in this arena, performance in ML tasks will far and away outstrip that from any competitor for many years to come.

At first the most visible will be AR titles since they tend to be very flashy. But AI titles will slowly begin to gain traction. Other platforms will be left in the dust in terms of performance. (Watch the Serif Affinity Photo demo in the WWDC keynote video time 1:40:10 - to see just how astoundingly fast the iPad Pro is.)

With these tools hardware and software Apple will assure itself of being far and away the leader in basic platform technology. This will allow them to attract new customers and encourage upgrades. Exactly what the investor wants.

Disclosure: I am/we are long IBM, AAPL.

I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it (other than from Seeking Alpha). I have no business relationship with any company whose stock is mentioned in this article.

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Artificial Intelligence: Apple's Second Revolutionary Offering - Seeking Alpha

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

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

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

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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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.

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Artificial intelligence is infiltrating ad tech - Digiday

Advances in Artificial Intelligence (AI) have dominated both tech and business stories this year. Industry heavyweights such as Stephen Hawking and Bill Gates have famously voiced their concern with blindly rushing into AI without thinking about the consequences.

AI has already proven that it has the power to outsmart humans. IBM Watson famously destroyed human opponents at a game ofJeopardy, and a Google computer beat the world champion of the Chinese board game,Go.

Google's AI team are taking no chances after revealing that they are developing a 'big red button' to switch off systems if they pose a threat to humans. In fact scientists at Google DeepMind and Oxford University have revealed their plan to prevent a doomsday scenario in their paper titledSafely Interruptible Agents.

Truth is indeed stranger than fiction and tech fans could be forgiven for nearly choking on their cornflakes this morning after hearing about a very public disagreement between the two tech billionaires. The argument is probably a good reflection of how people on both sides of the aisle feel about heading into the foggy world of AI.

In one corner, we have Mark Zuckerberg who believes AI will massively improve the human condition. Some say he is more focused on his global traffic dominance and short-term profits than the fate of humanity. Whatever your opinion, he does represent a sanguine view of futuristic technologies such as AI.

In the other corner, we have Tesla's Elon Musk who seems to be more aware of the impact our actions might have on future generations. Musk appears concerned that once the Pandora's box has been cracked open, we could unwittingly be creating a dystopian future.

Zuckerberg landed the first punch in a Facebook Live broadcast when he said

However, Elon Musk calmly retaliated by landing a virtual uppercut by tweeting "I've talked to Mark about this. His understanding of the subject is limited."

Whether you side with Musk and believe that AI will represent humanity's biggest existential threat or think Zuckerberg is closer to the truth when he said, AI is going to make our lives better, your view is entirely subjective at this point.

However, given the range of opinions around this topic, should we be taking the future of AI more seriously than we do today?

I will tell you that big businesses with large volumes of data are falling over themselves trying to install machine learning and AI driven solutions. However, right now, many of these AI driven systems are also the source of our biggest frustrations as consumers.

Are businesses guilty of rushing into AI based solutions without thinking of the bigger picture? There are several examples of things going awry like the Chat bots claiming to be a real person, or the spread of fake news, or being told you are not eligible for a mortgage because a computer says so.

There are also an increasing number of stories about AI not being quite as smart as some would believe it to be, or how often algorithms are getting it wrong or being designed to deceive consumers. For every great tech story, there is a human story about creativity and emotional intelligence that a machine can never match.

Make no mistake the AI revolution is coming our way, and large corporations will harvest the benefits of cultivating their big data initiatives. Anything that will eliminate antiquated processes of the past and enable business efficiency can only be a giant leap forward.

However, the digital transformation of everything we know is not going to happen overnight. That does not mean we shouldn't be vigilant about how our actions today could affect future generations.

Mr. Zuckerberg may be accused by some of acting in the interests of his social media platform, and that is quite understandable. Beneath every noble statement resides a hidden interest it is safe to assume that nowadays, unless one is Mahatma Gandhi, Dr. Martin Luther King or Nelson Mandela.

On the other hand, there are also the likes of Musk and Gates that are arguably looking beyond their own business interests.

I am no expert by any stretch of the imagination, but I do ask if we need more of us to question how advancements in technology are providing advantages for the few rather than the many?

Lets build on Elon Musks point of view for a moment. I wonder if we should be concerned that a dystopian future awaits us on the horizon? Will the machines rise and turn on their masters?

AI is no longer merely a concept from a science fiction movie. The future is now. The reality is that businesses need to harness this new technology to secure a preemptive competitive advantage. Time-consuming, laborious and automatable tasks can be performed better and faster by machines that continuously learn, adapt and improve.

The current advances in technology have unexpected parallels with the industrial revolution that helped deliver new manufacturing processes. 200 years ago, the transition from an agricultural society to one based on the manufacture of goods and services dramatically increased the speed of progress.

Steel and iron replaced manual labor with mechanized mass production hundreds of years ago. That is not unlike the circumstances facing businesses today. The reality is that as old skills or roles slowly fade away, there will be a massive shortage of other skills and new roles relevant to the digital age.

Ultimately, we have a desire to use technology to change the world for the better in the same way that the industrial revolution changed the landscape of the world forever. The biggest problems surrounding market demand and real world needs could all be resolved by a new generation of AI hardware, software, and algorithms.

After years of collecting vast quantities of data, we are currently drowning in a sea of information. If self-learning and intelligent machines can turn this into actionable knowledge, then we are on the right path to progress. Upon closer inspection, the opportunities around climate modeling and complex disease analysis also illustrate how we should be excited rather than afraid of the possibilities.

The flip side of this is the understanding that no thing is entirely one thing. The risks versus rewards evaluation and the fact that researchers are talking about worst case scenarios should be a positive thing. I would be more concerned if the likes of Facebook, Google, Microsoft and IBM rushed in blindly without thinking about the consequences of their actions. Erring on the side of caution is a good thing, right?

Demis Hassabis is the man behind the AI research start-up, DeepMind, which he co-founded in 2010 withShane LeggandMustafa Suleyman.DeepMind was bought by Google in 2014. Demis reassuringly told the UK's Guardian newspaper:

It would appear that all bases are being covered and we should refrain from entering panic mode.

The only question the paper does not answer is what would happen if the robots were to discover that we are trying to disable their access or shut them down? Maybe the self-aware machine could change the programming of the infamous Red Button. But that kind of crazy talk is confined to Hollywood movies, isnt it? Lets hope so for the sake of the human race.

Those of us that have been exasperated by Facebook's algorithm repeatedly showing posts from three days ago on their timelines will tell you that much of this technology is still in its infancy.

Although we are a long way to go before AI can live up to the hype, we should nevertheless be mindful of what could happen in a couple decades.

Despite the internet mle over the impact of AI between the two most powerful tech CEOs of our generation, I suspect like anything in life, the sweet spot is probably somewhere in the middle of these two contrasting opinions.

Are you nervous or optimistic about heading into a self-learning AI-centric world?

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Musk vs. Zuck - The Fracas Over Artificial Intelligence. Where Do You Stand? - HuffPost

High above the Balfour interchange on State Route 4 in Brentwood, a drone buzzes, its sensors keeping a close watch on the volumes of earth being moved to make way for a new highway bypass. In Pittsburg, a camera perched on the dash of car driving through city streets periodically snaps pictures of potholes and cracks in the pavement. And, at the corner of Harbor and School streets in the same city, another camera monitors pedestrians, cyclists and cars, where 13-year-oldJordyn Molton lost her life late last year after a truck struck her.

Although the types of technology and their goals differ, all three first-of-their-kind projects in Contra Costa County aim to offer improvements to the road construction and maintenance industry, which has lagged significantly behind other sectors when it comes to adopting new technology. Lack of investment stifled innovation, said John Bly, the vice president of the Northern California Engineering Contractors Association.

But, with the recent passage of SB1, a gas tax and transportation infrastructure funding bill, thats all set to change, he said.

You may see some of these high-tech firms find new market niches because now you have billions of dollars going into transportation infrastructure and upgrades, he said. Thats coming real quick.

Its still so new that Bly was hard-pressed to think of other areas where drone and artificial intelligence software is being integrated into road construction work in the state. The pilot programs in the East Bay are cutting edge, he said.

At the Contra Costa Transportation Authority, Executive Director Randy Iwasaki has been pushing to experiment with emerging technology in the road construction and maintenance industry for several years. So, when the authoritys construction manager, Ivan Ramirez, came to him with an idea to use drones in its $74 million interchange project, Iwasaki was eager to try it.

We often complain we dont have enough money for transportation, Iwasaki said, adding that the use of drones at the interchange project in Brentwood would enable the authoritys contractors to save paper, save time and save money.

Thats because, traditionally, survey crews standing on the edge of the freeway would take measurements of the dirt each time its moved. The process is time consuming and hazardous, Ramirez said. But its only the tip of the iceberg when it comes to potential applications for the drones technology, which could also be used to perform inspections on poles or bridges and perform tasks people havent yet thought of.

As you begin to talk to people, then other ideas begin to emerge about where we might be going, and its propelling more ideas for the future, Ramirez said. By not having surveyors on the road, or not having to send an inspector up in a manlift way up high or into a confined space, not only is it more efficient, but it will provide safety improvements, as well.

Meanwhile, in Pittsburg, the city is working with RoadBotics on a pilot program to better manage its local roads. The company uses car-mounted cellphone cameras to snap photos of street conditions before running that data through artificial intelligence software to create color-coded maps showing which roads are in good shape, which need monitoring and which are in need of immediate repairs.

The companys goal is to make it easier for city officials to monitor and manage their roads, so small repairs dont turn into complete overhauls, said Mark DeSantis, the companys CEO. Representatives from Pittsburg did not respond to requests for comment.

The challenge of managing roads is not so much filling the little cracks, thats not much of a burden, DeSantissaid. The real challenge is when you have to repave the road completely. So, the idea is to see the features on the road and see which ones are predictive of roads that are about to fail.

At the same time, Charles Chung of Brisk Synergies is hoping to use cameras and artificial intelligence software in a different way seeing how the design of the road influences how drivers behave. At the corner of Harbor and School streets, the company installed a camera to watch how cars, cyclists and pedestrians move through the intersection and to identify why drivers might be speeding. In particular, the company is also trying to determine how effective crossing guards are at slowing down cars, he said.

It is still in the process of gathering data on that intersection and writing its report, but Chung said it was able to use the software in Toronto to document a 30 percent reduction in vehicle crashes after the city made changes to an intersection there. Before, documenting the need for changes would require special crews to either monitor the roads directly or watch footage from a video feed, both of which take time and personnel.

While only emerging in a handful of projects locally, these types of technology will become far more prevalent soon, said Bart Ney of Alta Vista Solutions, the construction-management firm using drones on the SR 4 project.

Were at the beginning of the wave, he said. Like any disruptive technology, there is a period when you have to embrace it and take it into the field and test it so it can achieve what its capable of. Were on the brink of that happening.

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Roadwork gets techie: Drones, artificial intelligence creep into the road construction industry - The Mercury News

Artificial intelligence, machine learning and robotics are making some real money for stock investors, and beating the market.

The Global X Robotics and Artificial Intelligence ETF (BOTZ) is up 30 percent this year and the ROBO Global Robotics and Automation Index (ROBO) is up 25 percent. That's roughly three times the Dow Jones industrial average's 9 percent rise and twice the S&P 500's 11 percent climb.

"Between the tech exposure and the international exposure, that's helped the group pretty well," said Jack Ablin, chief investment officer at BMO Private Bank. "Certainly thematically it's in a sweet spot."

The upward trend in robotics and artificial intelligence stocks is one proponents say, in the long-term, could top the so-called FANG stocks Facebook, Amazon.com, Netflix and Google parent Alphabet. Each FANG stock has rallied 20 to 50 percent this year and the companies are increasingly focused on using technologies such as artificial intelligence, or AI, to develop their businesses.

"In our opinion, robotics, automation, AI [RAAI] is really the next FANG trade if you will," William Studebaker, president and CIO at Robo Global, told CNBC.

"All the FANG companies are really redefining their business as AI first and they're investing" in these companies, Studebaker said. "We're selling the tech they're using to enable their business."

Relative performance of ROBO and BOTZ to the S&P 500 (year to date)

Source: FactSet

The tech-heavy Nasdaq composite has soared 19 percent in 2017 to hit a record high this week. More than half of the S&P 500's technology sector sales come from overseas, where economic growth has largely picked up more than in the U.S.

Many of the robotics or machine learning-focused companies in the BOTZ and ROBO ETFs are not based in the U.S., helping explain some of the funds' outperformance.

For BOTZ, Japanese companies compose nearly half of the 29-stock fund, followed by the U.S. and Switzerland, according to its fact sheet. More than half of ROBO's stocks are based outside North America.

The top 10 holdings of BOTZ include Intuitive Surgical, which received a $1,000 price target from Goldman Sachs in May, Mitsubishi Electric, Nvidia and Keyence. ROBO's major holdings include Swiss-based industrial company Abb, Chinese industrial name HollySys and U.S. health care company Accuray, according to a fact sheet.

"Many of these companies are not followed by Wall Street or underfollowed by Wall Street," Studebaker said. "This is an industry that's evolving and so it's going to explode."

Investors are starting to get interested as well. Studebaker said ROBO added $1 billion in assets under management over the last 12 months, while Global X said BOTZ's assets under management leaped from $1.5 million at its launch in September 2016 to $236 million Monday.

That jump in assets under management makes BOTZ the youngest fund in Global X's top 10 largest funds, according to Jay Jacobs, director of research and vice president at Global X Funds. "It's really hitting this inflection point," he said.

Increased focus on artificial intelligence is already showing up in the tech giants' earnings calls.

Google parent Alphabet reported better-than-expected second-quarter results after the close Monday. Its shares fell Tuesday on worries that rising traffic acquisition costs will hit future profit growth, but UBS analyst Eric Sheridan wrote in a Monday report that he still holds a long-term constructive view on Alphabet given its focus on artificial intelligence and machine learning.

Alphabet's earnings call was also the third straight quarter in which Google CEO Sundar Pichai mentioned artificial intelligence, Gene Munster, once a prominent Apple analyst, pointed out Tuesday.

"Google is betting on the right long-term trends (Google, AI, AR, VR)," Munster said in a note from his new firm Loup Ventures, a venture capital firm focused on artificial intelligence, robotics, virtual reality and augmented reality.

Facebook is scheduled to report quarterly earnings Wednesday, and Amazon.com on Thursday.

Netflix CEO Reed Hastings said on the video streaming company's earnings call last week that the company uses algorithms for personalization.

To be sure, since terms like artificial intelligence and robotics have become buzzwords, investors will need to do their research to determine leaders in the industry,

The number of U.S. corporate earnings call transcripts mentioning the words jumped to 124 last quarter, up from 107 in the first quarter and 50 in the second quarter of 2016, according to a search using AlphaSense. The analysis covered U.S. companies with more than $2 billion in market capitalization.

"In the tech boom," BMO's Ablin said, "random companies would put 'dotcom' at the end of their name just to prove they're different."

Disclaimer

CNBC's Tae Kim contributed to this report.

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Crushing the old economy: Robotics, artificial intelligence fund has tripled the Dow this year - CNBC