Monthly Archives: July 2017

Theres no shortage of dire warnings about the dangers of artificial intelligence these days.

Modern prophets, such as physicist Stephen Hawking and investor Elon Musk, foretell the imminent decline of humanity. With the advent of artificial general intelligence and self-designed intelligent programs, new and more intelligent AI will appear, rapidly creating ever smarter machines that will, eventually, surpass us.

When we reach this so-called AI singularity, our minds and bodies will be obsolete. Humans may merge with machines and continue to evolve as cyborgs.

Is this really what we have to look forward to?

AI, a scientific discipline rooted in computer science, mathematics, psychology, and neuroscience, aims to create machines that mimic human cognitive functions such as learning and problem-solving.

Since the 1950s, it has captured the publics imagination. But, historically speaking, AIs successes have often been followed by disappointments caused, in large part, by the inflated predictions of technological visionaries.

In the 1960s, one of the founders of the AI field, Herbert Simon, predicted that machines will be capable, within twenty years, of doing any work a man can do. (He said nothing about women.)

Marvin Minsky, a neural network pioneer, was more direct, within a generation, he said, the problem of creating artificial intelligence will substantially be solved.

But it turns out that Niels Bohr, the early 20th century Danish physicist, was right when he (reportedly) quipped that, Prediction is very difficult, especially about the future.

Today, AIs capabilities include speech recognition, superior performance at strategic games such as chess and Go, self-driving cars, and revealing patterns embedded in complex data.

These talents have hardly rendered humans irrelevant.

Reuters

But AI is advancing. The most recent AI euphoria was sparked in 2009 by much faster learning of deep neural networks.

Artificial intelligence consists of large collections of connected computational units called artificial neurons, loosely analogous to the neurons in our brains. To train this network to think, scientists provide it with many solved examples of a given problem.

Suppose we have a collection of medical-tissue images, each coupled with a diagnosis of cancer or no-cancer. We would pass each image through the network, asking the connected neurons to compute the probability of cancer.

We then compare the networks responses with the correct answers, adjusting connections between neurons with each failed match. We repeat the process, fine-tuning all along, until most responses match the correct answers.

Eventually, this neural network will be ready to do what a pathologist normally does: examine images of tissue to predict cancer.

This is not unlike how a child learns to play a musical instrument: she practices and repeats a tune until perfection. The knowledge is stored in the neural network, but it is not easy to explain the mechanics.

Networks with many layers of neurons (therefore the name deep neural networks) only became practical when researchers started using many parallel processors on graphical chips for their training.

Another condition for the success of deep learning is the large sets of solved examples. Mining the internet, social networks and Wikipedia, researchers have created large collections of images and text, enabling machines to classify images, recognize speech, and translate language.

Already, deep neural networks are performing these tasks nearly as well as humans.

But their good performance is limited to certain tasks.

Scientists have seen no improvement in AIs understanding of what images and text actually mean. If we showed a Snoopy cartoon to a trained deep network, it could recognize the shapes and objects a dog here, a boy there but would not decipher its significance (or see the humor).

We also use neural networks to suggest better writing styles to children. Our tools suggest improvement in form, spelling, and grammar reasonably well, but are helpless when it comes to logical structure, reasoning, and the flow of ideas.

Current models do not even understand the simple compositions of 11-year-old schoolchildren.

AIs performance is also restricted by the amount of available data. In my own AI research, for example, I apply deep neural networks to medical diagnostics, which has sometimes resulted in slightly better diagnoses than in the past, but nothing dramatic.

In part, this is because we do not have large collections of patients data to feed the machine. But the data hospitals currently collect cannot capture the complex psychophysical interactions causing illnesses like coronary heart disease, migraines or cancer.

So, fear not, humans. Febrile predictions of AI singularity aside, were in no immediate danger of becoming irrelevant.

AIs capabilities drive science fiction novels and movies and fuel interesting philosophical debates, but we have yet to build a single self-improving program capable of general artificial intelligence, and theres no indication that intelligence could be infinite.

Deep neural networks will, however, indubitably automate many jobs. AI will take our jobs, jeopardising the existence of manual labourers, medical diagnosticians, and perhaps, someday, to my regret, computer science professors.

Robots are already conquering Wall Street. Research shows that artificial intelligence agents could lead some 230,000 finance jobs to disappear by 2025.

In the wrong hands, artificial intelligence can also cause serious danger. New computer viruses can detect undecided voters and bombard them with tailored news to swing elections.

Already, the United States, China, and Russia are investing in autonomous weapons using AI in drones, battle vehicles, and fighting robots, leading to a dangerous arms race.

Now thats something we should probably be nervous about.

Marko Robnik-ikonja, Associate Professor of Computer Science and Informatics, University of Ljubljana

This article was originally published on The Conversation. Read the original article.

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Is Artificial Intelligence A (Job) Killer? | HuffPost - HuffPost

Claude Monet used brushes, Jackson Pollock liked a trowel, and Cartier-Bresson toted a Leica. Mario Klingemann makes art using artificial neural networks.

In the past few years this kind of softwareloosely inspired by ideas from neurosciencehas enabled computers to rival humans at identifying objects in photos. Klingemann, who has worked part-time as an artist in residence at Google Cultural Institute in Paris since early 2016, is a prominent member of a new school of artists who are turning this technology inside out. He builds art-generating software by feeding photos, video, and line drawings into code borrowed from the cutting edge of machine learning research. Klingemann curates what spews out into collections of hauntingly distorted faces and figures, and abstracts. You can follow his work on a compelling Twitter feed .

A photographer goes out into the world and frames good spots, I go inside these neural networks, which are like their own multidimensional worlds, and say Tell me how it looks at this coordinate, now how about over here? Klingemann says. With tongue in cheek, he describes himself as a neurographer.

Klingemanns one big project for Google so far is an interactive online installation launched in November that uses image recognition to find visual connections between any two images in a giant collection covering thousands of years of art historysay a roman sculpture and a Frida Kahlo self-portrait . While working in secret on a sequel to that project at Google, Klingemann has been exploring the potential of neurography in public on his own time. Many of his recent creations were made with a technique trendy among machine learning researchers called generative adversarial networks , which, given the right source material, can teach themselves to fabricate strikingly realistic digital images and audio files.

Some computer science researchers are using the method to fill in missing details in patchy radio telescope images. Others are using it to train systems to process health records without risking real patient data. [I dont quite understand. How would records be put at risk? Clarify?] Klingemann has harnessed it to generate images that combine the styles of 19th century portraits and 21st century selfies , and fabricating impressively realistic footage like this clip of 1960s French chanteuse Francoise Hardy.

Klingemann's work is in turn inspiring other artists. In a Barcelona show called My Artificial Muse earlier this month, artist Albert Barqu-Duran spent three days painting a fresco of an image Klingemanns software had generated from a stick figure modelled on John Everetts famous painting Ophelia .

All of which raises the perennial question: Is this art ? Klingemann says he and others using neural networks this way will have to gradually earn their place in the art world just as video and digital artists had to do over the last several decades. These new forms always have a hard time being accepted by the establishment, he says.

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Right now Klingemann has to work hard to find the training data that will cause his neural networks to produce interesting results. Hes built himself a Tinder-style interface to quickly work through piles of newly generated neurographs and find the few that strike him as any good. I produce a thousand images and maybe two or three are great, 50 are promising, and the rest are just ugly or repetitive, he says.

As you may have noticed, the images he does select typically come with more than a little of the uncanny about them; Klingemann has lost count of the times hes been told the faces and figures his code generates are reminiscent of Francis Bacons famously grotesque and disturbing work.

The comparison is apt. Its also evidence of how far artificial neural networks are from really understanding images or artnot that computers have warped minds. Im doing creepy right now because I cant do non-creepy, I wish I could, Klingemann says. In two or three years, the creepiness will go away, which might make it more creepy because we wont be able to distinguish from a photo or painted artwork. The uncanniest AI artist of all might be the one whose raw output doesnt look artificial.

Originally posted here:

A 'Neurographer' Puts the Art in Artificial Intelligence - WIRED

Worldwide spending on cognitive and artificial intelligence (AI) systems is predicted to increase 59.3% year-over-year to reach $12.5 billion by the end of 2017, according to an International Data Corporation (IDC) spending guide. That number is forecast to almost quadruple by 2020, when spending on AI is predicted to reach more than $46 billion.

If personalization was the marketing buzzword of 2016, then 2017 is the year of artificial intelligence. As more cloud vendors tout their own AI systems, however, could AI be over-hyped?

Joe Stanhope, vice president and principal analyst at Forrester, says a cultural dissonance exists with AI, thanks to science fiction, and that many people have a preconceived notion about what artificial intelligence really is.

A lot of people are talking a big game about AI and how it will change the world, but today its only applied in extremely discrete ways, says Stanhope. Theres a lot of hype around it.

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Stanhope says this overexposure creates a dissonance, compounded by marketers trust issues with AI.

Marketers have a right to be skeptical about artificial intelligence, says Stanhope, adding that it is imperative that they begin to educate themselves about AI, since it is highly complex and difficult to understand without a doctoral degree in statistics, math or engineering.

Stanhope recommends that marketers become "educated about AI techniques and algorithms to develop a functional understanding of how it works. By educating themselves, marketers can be more critical of vendors AI-driven applications.

AI gets thrown out quite a bit, but marketers need to get to the point where they can ask, 'what can your AI do for me now'?" says Stanhope. You need to be able to ask, and they [vendors] need to be able to define and validate that question.

Although it may not be as exciting as changing the world, Stanhope says there are very realistic applications for AI today. Humans have become the bottleneck in marketing, he says, and AI has the potential to make marketers and marketing better.

AI is an efficiency play, says Stanhope, describing how it helps marketers manage data, experiment with segmentation, and takes out the human drudgery of menial tasks. He recommends that marketers dip their toes in AI by applying it to one existing use case first, and then broadening the scope when new use cases become available and trust is built.

Youre not turning over the whole marketing team to a computer, says Stanhope.

Stanhope also recommends that AI marketers investigate whether their ESP offers some sort of AI function, as it is easier to evaluate an add-on solution than find a completely new product.

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Is Artificial Intelligence Over-Hyped? - MediaPost Communications

Note: This article first appeared on my Trend Investing Marketplace service on June 8. All data is therefore as of that date.

I wrote previously about the relatively new trend of Artificial Intelligence (AI) in my article. This time I plan to take a look at the main companies to consider for investing in AI, and select my top 5.

AI - "machines with brains"

Source

Many AI companies are unlisted and acquired before IPO

According to CB Insights, "over 200 private companies using AI algorithms across different verticals have been acquired since 2012, with over 30 acquisitions taking place in Q1'17 alone." Perhaps CB Insights will be next. The graph below gives further details. The left side list also gives an idea of the current AI leaders and acquirers.

Source: CB Insights

Alphabet Inc (NASDAQ: GOOG, GOOGL)

Wikipedia quotes: "According to Bloomberg's Jack Clark, 2015 was a landmark year for artificial intelligence, with the number of software projects that use AI within Google increased from a "sporadic usage" in 2012 to more than 2,700 projects."

Google's research projects often have the idea of automating everything, and connecting everything and everyone online (such as "Project Loon").

Google search is already using complex algorithms (e.g. RankBrain) and deep learning techniques.

Google's Home is a recent example of Google's move into AI with their inbuilt "Google Assistant" (similar to Siri, Alexa, Bixby, M, Cortana and Watson). The assistant responds to any sentence beginning "Hey Google". A key is the voice recognition technology and microphones, so that the assistant can correctly understand you and make appropriate responses.

Google announced its first chip, called the Tensor Processing Unit (TPU), in 2016. That chip worked in Google's data centers to power search results and image-recognition. A new version will be available to clients of its cloud business.

Google is a leader in autonomous car systems, which uses plenty of AI. It helps they already have Google maps. Google also have android auto car entertainment and internet capability.

Some AI acquisitions include: DNNresearch (voice and image recognition), DeepMind Technologies (deep learning, memory), Moodstock (visual search), Api.ai. (bot platform), Kaggle (predictive analytics platform).

Amazon Echo Dot has Alexa, Google Home has Hey Google, and Apple smartphone has Siri

Source

Amazon (NASDAQ: AMZN)

Amazon's home speaker Echo (and Echo Dot) and personal assistant "Alexa" is an example of Amazon's move into AI. Alexa is perhaps the most successful AI powered personal assistant thus far, especially as it is able to do over 10,000 online and offline functions.

Amazon Web Services ("cloud") offers deep-learning capabilities, allowing users to use up to 16 of Nvidia's Tesla K80 GPUs.

Amazon's AI acquisitions include Orbeus (automated facial, object and scene recognition), Angel ai (Chatbots), and Harvest.ai (cyber security).

Advanced Micro Devices (NASDAQ:AMD)

AMD are a semiconductor manufacturer. The company's new AI chips are the Radeon Instinct series. AMD are also racing to put its chips into AI applications. AMD will release its ROCm deep-learning framework, as well as an open-source GPU-accelerated library called MIOpen. The company plans to launch three products under the new brand in 2017.

Apple (NASDAQ: AAPL)

Apple has been an AI leader with their voice (and face) recognition software used by their personal assistant Siri (on your smartphone), introduced in 2011. In October 2016, Apple hired Russ Salakhutdinov from Carnegie Mellon University as its director of AI research.

Apple is working on a processor devoted specifically to AI-related tasks. The chip is known internally as the "Apple Neural Engine". Apple have an autonomous vehicle development team that uses AI, and they are also said to be working on augmented reality using AI chips. Apple is reportedly developing a specific AI chip for mobile devices.

Apple is also a leader in the areas of Virtual Reality ((VR)) and Augmented Reality ((AR)) headsets. Apple's ARKit will available soon and may one day may replace the smartphone. Apple iPhone users could get their first taste of AR technology later this year. The 10-year anniversary Apple iPhone will be enhanced with AR features.

Apple has made several AI acquisitions including Perceptio (deep learning technology for smartphones, that allows phones to independently identify images without relying on external data libraries), Emotient (can assess emotions by reading facial expressions), and RealFace (facial recognition).

VR and AR headsets are the next potential big thing

Source

Baidu (NASDAQ: BIDU)

Baidu are the Google of China, so not surprisingly they have followed in Google's footsteps developing deep learning search functionality, as well as autonomous driving.

Facebook (NASDAQ: FB)

Mark Zuckerberg has become very interested in AI, since initially using it for simple tasks. Facebook has chatbots and Zuckerberg has developed his own personal assistant "M". The Facebook site uses AI to then direct targeted advertising to match your likes.

Facebook holds more than $32 billion in cash on its balance sheet and produced more than $13 billion in free cash flow over the last year. The company does not pay any dividends and has no debt. This means Facebook can pretty much buy up any rivals or promising new AI or tech companies. Some acquisitions include Face.com (facial recognition), Masquerade (a selfie app that lets you do face-swaps), Occulus (virtual reality), Eye Tribe (eye tracking software) and Zurich Eye (enables machines to see).

Facebook's Mark Zuckerberg has recently said that "the next big thing is augmented reality." He sees a world where we use AR glasses to project an image like a computer screen. The tricky part is the mouse, and so Facebook's team are looking at using direct brain to glasses technology, or something like eye movements to control your screen.

International Business Machine's (NYSE: IBM)

IBM should not be underestimated in AI, as they have previously led the AI industry. They became famous in this area when their supercomputer/personal assistant Watson was able to beat two quiz champions live on television. Apparently Watson can read 40 million documents in 15 seconds.

IBM have acquired Cognea (virtual assistants with a depth of personality), Alchemy deep learning, natural language processing (specifically, semantic text analysis, including sentiment analysis) and computer vision (specifically, face detection and recognition), and Explorys (a healthcare intelligence cloud company that has built one of the largest clinical data sets in the world, representing more than 50 million lives).

Intel (NASDAQ: INTC)

Intel are the global leading semiconductor manufacturer, with a dominant position in the desktop/PC market.

Intel also acquired Indisys (intelligent dialogue systems), Saffron (cognitive computing), Itseez (vision systems), and recently paid $15.3b for Mobileye (used in autonomous driving). Intel also recently spent $400 million to buy deep-learning startup Nervana. The company intends to integrate Nervana technology into Xeon and Xeon Phi processor lineups. Intel claims Nervana will deliver up to a 100-fold reduction in the time it takes to train a deep learning model within the next three years.

Microsoft (NASDAQ: MSFT)

Microsoft's background is in PC/desktop software (Office etc), and in gaming (XBox).

AI was first adopted by hyperscale data centers such as Microsoft, Facebook, and Google, which used AI for image recognition and voice processing. Microsoft's Azure offers deep-learning capabilities support for up to four of Nvidia's slightly older GPUs.

Microsoft also have a personal assistant name "Cortana".

Microsoft AI acquisitions include : Netbreeze (social media monitoring and analytics), Equivio (machine learning), SwiftKey (analyzes data to predict what a user is typing and what they'll type next), Genee (an AI app that acts as a digital personal assistant to schedule meetings), and Maluuba (deep learning, natural language processing).

Nvidia (NASDAQ: NVDA)

Nvidia design and sell industry leading AI chips, which puts them at the top of the AI pyramid. They collaborate, design and sell their various types of chips to almost all the top tech companies.

Nvidia's background is as a designer and seller of graphic processing units ((GPUs)) to dominate the gaming industry.

The company has expanded their products to include AI chips such as the Tesla P100 GPU, making them a leader in AI. Those chips are popular with data centers, and autonomous and semi-autonomous vehicles. Tesla recently decided to drop Mobileye and go for Nvidia technology for their autonomous vehicles.

Bloomberg summarizes well; "Nvidia has become one of the chipmakers leading the charge to provide the underpinnings of machine intelligence in everything from data centers to automobiles. As the biggest maker of graphics chips, Nvidia has proved that type of processor's ability to perform multiple tasks in parallel has value in new markets, where artificial intelligence is increasingly important."

Qualcomm (NASDAQ: QCOM)

Qualcomm's revenue has mostly come from wireless modem licensing fees, especially as a key supplier to Apple. However the company are currently facing legal (U.S. FTC and Apple litigation) and other issues such as pressure on declining smartphone related revenues. Qualcomm are also operate in the areas of IoT, security and networking industries. Given the coming boom in autonomous vehicles, Qualcomm are in the process of buying NXP Semiconductors (NASDAQ:NXPI) (for $47b), the leader in high-performance, mixed-signal semiconductor electronics - and a leading solutions supplier to the automotive industry.

Qualcomm Inc.'s latest Snapdragon chip for smartphones has a module for handling artificial intelligence tasks.

Samsung (OTC: SSNLF)

Samsung are the global number 2 semiconductor manufacturer, and the global number 1 smartphone seller. The rise of AI will lead to a huge increase in demand for both computer processing chips and memory chips - which Samsung can supply.

Samsung's "Bixby" is similar to Apple's personal assistant Siri.

In virtual reality (VR) headsets, as of Q1 2017, Samsung is the current market leader with 21.5% market share. Sony is second with 18.8%, followed by HTC with 8.4%, Facebook with 4.4%, and TCL 4%. Total worldwide shipments of augmented reality and virtual reality headsets reached 2.3M in the first quarter, according to IDC. The VR headsets represented over 98% of the sales. An IDC report from March predicts the amount of shipped AR and VR headsets will reach 99.4M units by 2021.

Tesla (NASDAQ: TSLA)

Tesla are the global leader in Autonomous Vehicles ((AVs)). As AVs progress from stage 1 to stage 5 (full autonomy) Tesla can be a large beneficiary in terms of electric car sales and transport as a service (taxi company). Tesla may also benefit from selling their AV technology to other car manufacturers, or if they expand into using AI in the home as they already have the power wall and solar roof. Meaning Tesla may end up being your taxi company, your energy supplier, and your content provider both in your car and home. All of these can be run using AI programs from your smartphone. Elon's latest venture is neural networks - wherein he is looking at how we can connect the brain directly to a device.

Others to consider

Ebay Inc (NASDAQ:EBAY), General Electric (NYSE:GE), Nice Ltd (NASDAQ:NICE), Oracle (NYSE:ORCL), Salesforce.com (NYSE:CRM), Skyworks Solutions (NASDAQ:SWKS), Softbank (OTC:SFBTF)(they bought out chip designer ARM, and own 4.95% of Nvidia), Sophos Group (LN:SOPH) (IT security), and Twitter (NYSE:TWTR).

The companies that make the hardware behind the AI boom - especially the optics (transceivers, transponders, amplifiers, lasers and sensors)

With all booms often it is wisest to buy those that make the picks and shovels. In this case it is the optics (transceivers, transponders, amplifiers, lasers and sensors), cameras, semiconductors and so on. I have already discussed Samsung, Nvidia, Intel, Qualcomm, and AMD above, as they will do well from semiconductor design and sales.

Some of the major optics providers that can do very well include:

Note: I will most likely write a separate article on how to benefit from the AI boom by buying the picks and shovels stocks behind the boom.

Conclusion

AI will be invisible, yet it will be everywhere. AI appears on our smartphones, with virtual assistants, augmented and virtual reality headsets, robots, in data centers, and in semi and fully autonomous vehicles.

My top 5 AI stocks to play the coming AI boom are Apple, Samsung, Alphabet Google, Facebook, and Nvidia. If I could find the next Nvidia or listed small cap AI company with potential I would include them in a top 6. For now I have not yet found, as mostly they get bought out by the tech giants before going public.

Apple and Samsung are chosen as they are the global top two smartphone sellers (the smartphone and AR/VR devices will mostly be the operating systems for mass market retail AI), they have very loyal customer bases to cross-sell new AI products to, and also control what chips they use in their devices. Apple may move towards their own AI chip, and Samsung are the global number 2 chip maker already.

Alphabet Google and Facebook dominate the internet, and therefore have a large influence on the retail and business market. They are both already leaders in AI, with the financial backing to buy out any competitive threats. We could perhaps add Amazon to this group also.

Nvidia are the clear chip design leader in the AI space, and have an excellent track record. They are a must have in any AI portfolio. AMD would be a cheaper alternative for those worried about Nvidia's valuation.

Finally an equally wise move would be to buy the "pick and shovel" makers behind the boom such as Applied Optoelectronics and Fabrinet.

I am interested to hear your favorite AI stock and why. As usual all comments are welcome.

Disclosure: I am/we are long GOOG, FB, SSNLF, FN, AAOI.

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.

Additional disclosure: The information in this article is general in nature and should not be relied upon as personal financial advice.

Editor's Note: This article discusses one or more securities that do not trade on a major U.S. exchange. Please be aware of the risks associated with these stocks.

Continued here:

A Look At The Artificial Intelligence Companies And My Top 5 - Seeking Alpha

Barron's

What Does Baidu Have In Its Artificial Intelligence Pipeline? Barron's As COO of Baidu Mr. Qi Lu said, Baidu will all in AI and continues to emphasize AI (artificial intelligence) as the key strategic focus given the window of opportunity for Baidu. Its AI strategy will be empowered by DuerOS (conversational AI ... China can seize opportunity to lead global AI development, Baidu executives sayCNBC Baidu embraces artificial intelligence, set to build an open ecosystemGlobal Times all 93 news articles »

The rest is here:

What Does Baidu Have In Its Artificial Intelligence Pipeline? - Barron's