We have nothing to fear from artificial intelligence, says pioneer | The Times

Follow on Twitter as @olivernmoody

"Elon Musk and Bill Gates are among those who fear that artificial intelligence threatens mankind, but such critics have been rebuked by the head of Google’s project to make a machine that can learn and create like a person." summarizes Oliver Moody, Science Correspondent.

Demis Hassabis says the grim vision of the future posed by AI were largely based on ignorance.
Photo: Jung Yeon-Je/Getty Images

Deep Mind, a British business bought by Google three years ago, is taking on problems from diseases to inefficiencies in the National Grid, just as others say that a superhuman AI could be our deadliest invention.

Demis Hassabis, Deep Mind’s chief executive, said that apocalyptic visions of the future were mostly based on ignorance. “I don’t think it’s very helpful for other people who are incredible in their domains commenting on something they actually know very little about,” he said, “but because they are quite big celebrities now, more than just scientists or businessmen, it gets picked up a lot.”

Dr Hassabis told an event organised by the Cambridge Society for the Application of Research: “There are some valid worries and I think these are research questions of vulnerability and interpretability, but I think this general meme of fearfulness doesn’t help reasoned debate.

“ It actually drives that debate away. I’ve told all of those people you mentioned [Mr Musk and Mr Gates] that it’s not very helpful. Some of them have moderated their comments, but others haven’t.”
Read more...

Source: The Times

Learning not to ‘over think’ predictive analytics | Information Management

"Most firms are more ready than they realize to implement the technology, and they fail to see the countless opportunities at hand." according to David Weldon, editor-in-chief of Information Management.   

Photo: Information Management

The predictive analytics and machine learning markets are projected to grow at a rate of 15 percent annually through 2021, yet many organizations fail to reap full benefits from their investments. The problem is often that the organization makes the process too complicated.

The irony is that most large organizations are more prepared to implement and use predictive analytics and machine learning than they think, says Mike Gualtieri, a research analyst with Forrester Research. 

Gualtieri has just published a Forrester Wave report on predictive analytics and machine learning, “The Forrester Wave: Predictive Analytics and Machine Learning Solutions, Q1 2017.” In it, Gualtieri notes that organizations that want to leverage artificial intelligence need to start with a predictive analytics and machine learning solution.

“Predictive models created using machine learning are already commonly used for marketing, customer intelligence, and risk models,” Gualtieri says. “The teams of data scientists that create these models are in the know. But, often the employees such as enterprise architects who are charged with investigating AI don't understand that machine learning models are fundamental building blocks of AI.”

That is unfortunate, Gualtieri says. “There are hundreds, if not thousands, of opportunities to use machine learning models in business processes and customer experiences. This is not day one, but it is still only day 2. There is tremendous opportunity today, but most enterprises struggle about how to think about AI. They are thinking too big. Successful machine learning models is about predicting one simple thing that can have a big impact on the business such as the next best product to recommend for an individual customer.”

Forrester forecasts a 15 percent compound annual growth rate (CAGR) for the PAML market through 2021. That’s a conservative estimate, given that the PAML category includes and overlaps with AI and deep learning. Gualtieri says the category continues to be hot since most large enterprises want the power to predict and have only scratched the surface of what is possible. 
Read more... 

Source: Information Management  

Smiths Detection Inc. Partners with Duke University in Deep Learning Research for Airport Screening | Business Wire

Smiths Detection Inc. (SDI) announced that it is partnering with the Duke University Edmund T. Pratt Jr. School of Engineering, Department of Electrical and Computer Engineering, in a “deep learning” digital solution project to advance airport checkpoint x-ray system screening capabilities.   

Photo: FreeDigitalPhotos.net

The U.S. Transportation Security Administration (TSA) has entered into a contract with Duke University for this deep learning initiative to refine and apply state-of-the-art machine learning techniques in the security space. In this case, Duke and SDI will partner to apply the deep learning methodology to enhance the capabilities of checkpoint x-ray systems.   

Dan Gelston, President of SDI, said, “We must continue to invest in digital solutions to remain at the forefront of technology. This partnership, combined with our focus on innovation and experience in threat detection, leads the security industry in the development of state-of-the-art methods to help make the world a safer place.”

The principal investigators for this effort will be Professor Lawrence Carin at Duke University and Dr. Kristofer Roe of Smiths Detection Inc. Professor Carin has more than 27 years of experience and is also Vice Provost of Research for Duke. Dr. Roe, currently Director, R&D – Imaging for SDI, is responsible for imaging technology research and development in the areas of screening and aviation security. Dr. Roe was also the principal investigator of the NextGen Checked Baggage Program (Manhattan II) program with TSA.

Smiths Detection, part of Smiths Group, is a global leader in threat detection and screening technologies for military, air transportation, homeland security and emergency response markets. Our experience and history across more than 40 years at the frontline, enables us to provide unrivalled levels of expertise to detect and identify constantly changing chemical, radiological, nuclear and explosive threats, as well as weapons, dangerous goods, contraband and narcotics.

Our goal is simple – to provide security, peace of mind and freedom of movement upon which the world depends. 
For more information visit www.smithsdetection.com

Source: Business Wire (press release)

What Is The Best Way To Learn Machine Learning Without Taking Any Online Courses? | Forbes

"What is the best way to start learning machine learning and deep learning without taking any online courses? originally appeared on Quora: the place to gain and share knowledge, empowering people to learn from others and better understand the world." Quora, Contributor.

Answer by Eric Jang, Research engineer at Google Brain, on Quora:

Photo: Shutterstock

Let me first start off by saying that there is no single “best way” to learn machine learning, and you should find a system that works well for you. Some people prefer the structure of courses, others like reading books at their own pace, and some want to dive right into code.

I started with Andrew Ng’s Machine Learning Coursera course in 2012, knowing almost zero linear algebra and nothing about statistics or machine learning. Note that although the class covered neural networks, it was not a course on Deep Learning. I really enjoyed how the course formulated “machine learning” as nothing more than numerical optimization.

Deep Learning book

If online courses are too slow for you, the best consolidated resource is probably Deep Learning book by Goodfellow, Bengio, and Courville. It has a few chapters dedicated to the basics (sort of like what is covered in Ng’s class) and then jumps into practical DNNs.

Murphy’s Probabilistic
Machine Learning textbook

A statistical/mathematically rigorous background is not required to do useful Deep Learning work, but it really helps to formulate hypotheses about why models are/are not working, and what might help. Murphy’s Probabilistic Machine Learning textbook is a great foundation for mathematically rigorous ML (and has great diagrams too!) 

After you finish the DL book, you can “specialize” into one of the subfields/sub-subfields of Deep Learning, by implementing some of the papers yourself. Some example topics:

• Bayesian Deep Learning (combining neural nets with graphical models)
• Deep Reinforcement Learning (AlphaGo, Atari-playing AI, Robotics)
• Generative Models (GANs, PixelCNN, VAEs)
• Adversarial Methods (GANs, Actor-Critic)
• Theory of Deep Learning
• Computer Vision
• NLP/Speech (translation, captioning, seq2seq models)
• Symbolic reasoning (e.g. proof-solving)
• Recurrent Neural Networks (e.g. LSTMs, external memory, attention)
• Applications (solving domain-specific problems like classifying cancer, protein folding, lip reading from video)
• Meta-learning / learning-to-learn (Synthetic Gradients, Pathnet)

The Deep Learning field has dramatically expanded in the last few years, to the point where it’s not realistic to grok all the subfields of Deep Learning in a short amount of time.
Read more... 

Source: Forbes

Machine learning: Should we be excited or fearful for our jobs? | Siliconrepublic.com

How machine learning can drive efficiency rather than drive people out of their jobs, insist Nicola Mortimer, head of business products, marketing and operations at Three Ireland. 

Photo: vectorfusionart/Shutterstock

2017 will be a year of dramatic acceleration in the pace of development of artificial intelligence (AI) and the internet of things (IoT). Machine learning is predicted to be an integral part of more than 300m new smartphones sold this year. So, should we be excited or fearful for our jobs?

It has been predicted that machine learning capabilities will be present in more than 20pc of smartphones sold globally in 2017. With few devices more ubiquitous in the developed world than the smartphone, machines that learn will now be at the fingertips of a large percentage of the population.

What will the increasing development of machine learning, AI, machine-to-machine (M2M) communication and IoT mean for business and industry, and the people who work within them?

One of the important things to realise about the way machines learn, and therefore develop intelligence, is that it is not a mysterious, science-fiction process. Machine learning produces, in effect, nothing more than glorified data crunchers.

Machines that learn can learn only from the data they receive and analyse. What makes them such quick learners and so apparently intelligent is that – unlike humans – they can receive, absorb and analyse all the relevant data in the world at incredibly high speed, and then use it to inform the decisions they make.

Importantly, for the development of true AI, these machines are now also beginning to learn from the data and adapt their behaviour accordingly. For example, at the simplest level, Google Translate now adapts as it learns, to make its translations more accurate.

At the other extreme, data gathered from the journeys of Tesla test vehicles is uploaded to the cloud and made available to all Tesla driverless cars. This means if a test vehicle has driven a stretch of road, when another Tesla vehicle travels it for the first time, it will know how to brake for a specific corner, which lane to take for a turn, even what driving line to take to avoid a large pothole.
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Source: Siliconrepublic.com

Highest paying jobs for developers go to Machine Learning specialists | Computer Business Review

Photo: Joe Clark

"A new report from Stack Overflow has found that Machine Learning specialists have the highest paying jobs among developers." reports Joe Clark, Reporter.

Photo: Computer Business Review

A new report has found that Machine Learning Specialists typically have the highest paying jobs among developers in the UK and Ireland.

The Stack Overflow ‘Developer Hiring Landscape Report’, discovered that, on average, developers earn 45% above the national average, and Machine learning specialists earn 25% more than the average developer salary.

In 2016/17 the UK national average salary was £28,000 whereas developers enjoyed a much larger pay of £44,000. At the higher end of the scale the average Machine learning specialist could enjoy a salary of £56,000, and at the lower end graphic designers were paid £35,000 on average – still 20% above the national average.

The Stack Overflow report seems to confirm the findings of the Tech Nation 2017 report, which found that the UK digital economy is currently growing at a rate twice that of the rest of the country, with above average salaries and an increase in job positions.

Kevin Troy, Director of Insights at Stack Overflow, said: “This year’s Stack Overflow’s Developer Hiring Landscape Report is the most comprehensive survey of the developer workforce to date, with more than 4,700 responses from developers across the United Kingdom and Republic of Ireland, and more than 64,000 responses globally.”
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Source: Computer Business Review 

Artificial Intelligence, Machine Learning, and Deep Learning and How they Differ from One Another | TrendinTech

"These are three terms that are heard all the time now, but often people still get confused about what each one really entails." inform Ryan Young | TrendinTech.

Photo: ShutterStock

Below is a quick rundown of each that will hopefully things out a little and give you a real insight as to what these interchangeable terms mean.

Artificial Intelligence, or AI for short, is the broadest way in which to describe computer intelligence.  Back in1956 it was described as “Every aspect of learning or any other feature of intelligence can in principle is that a machine can be made to simulate it” at the Dartmouth Artificial Intelligence Conference. AI can come in various forms including game-playing computer programs and voice recognition systems...

Machine learning (ML) is a subdivision of AI that involves machines deciphering data and learning for themselves.  It’s used a lot throughout the businesses of today as is very efficient when used in areas such as speech, object, and facial recognition, translation, and other tasks...

That then leads us to Deep Learning, which is a subdivision of ML. It too makes use of certain ML techniques by tapping into neural networks that mimic a human’s decision making.
Read more...  

Source: TrendinTech

Robots, artificial intelligence boom may cost US, UK at least 1/3 of their jobs: Study | CNBC

"The robot invasion is coming, and its gunning for at least 30 percent of the jobs in US, UK" notes CNBC.
 

Photo: CNBC

Up to one-third of Britishjobscould be taken over by robots by the early 2030s, impacting 10 million Britons but with women less likely to face redundancy, a UK study showed.

The research, by accountancy firm PricewaterhouseCoopers LLP, found 30 percent of UK jobs could face automation compared to 38 percent in the United States, 35 percent in Germany, and 21 percent in Japan.

Researchers, however, said this wouldn't necessarily lead to less employment as jobs may change rather than disappear.

But the distinction between men and women was clear, with PwC estimating 35 percent of men's jobs were at risk compared to 26 percent of women's because of the high number of women in sectors requiring social skills like education and health.
Read more...

Source: CNBC

Can artificial intelligence make you a better tweeter? | Recode

Follow on Twitter as @KurtWagner8

Kurt Wagner, business and tech journalist since 2012 says, "An AI startup called Post Intelligence hopes it can."

Photo: Shutterstock / Willyam Bradberry

I‘ve tweeted nearly 5,000 times in my life, which certainly feels like a lot.

But last week I did something on Twitter I’ve never done before: I used artificial intelligence to help me decide what to tweet. More specifically, I used a service called Post Intelligence, which recommended links and photos to post, suggested the time of day I should post to get the best engagement, and even estimated the popularity of my tweets before I sent them based on the language I used in the tweet.

To do this, Post Intelligence, which used to be called MyLikes and has raised $11 million from Khosla Ventures, uses algorithms similar to those Twitter and Facebook use to determine what you see in your feed.

The company analyzed my Twitter account to determine the topics I tweet about most and calculated which of those topics also perform well with my followers. Then the AI went out and found tweets about those topics that were performing well on Twitter and suggested I share them, too.

The results: The AI-suggested tweets performed better than my normal ones. Kinda. 

I sent 24 tweets over a span of 9 days, 12 that included media suggested to me by Post Intelligence and 12 that including content I found on my own. (This excludes a lot of replies to tweets that I sent, and the three times I tweeted my own stories from Recode.)

The AI-powered tweets received an average of 7.2 favorites and 2.2 retweets apiece. My “original” tweets received 5.0 faves and 1.5 retweets, on average. On the surface, the AI appeared to be a noticeable help.

I also added 70 new followers in the 9-day stretch; I had averaged just 118 new followers per month in the six months prior.

But the engagement data is skewed: The AI suggestions led to my most popular tweet of the period, this gem about BBC girl and how she would be a badass reporter (or badass anything, from the looks of it), which generated a whopping 33 favorites and 11 retweets. 

Without that outlier, my AI-suggested tweets averaged 4.8 faves and 1.4 retweets, on average, almost exactly the same as the tweets I sourced on my own...

Kurt Wagner ends his article with the following: "I want to keep using Post Intelligence to see if a larger sample size changes my performance (or my mind). You can try it here as well. If I learn anything fun, I’ll be back to share it with you all." 

Read more...  

Source: Recode

SA-made solution helps Maths and Science students achieve good results | The South African

Photo: Caryn Edwards

"All you need is a cellphone." says Caryn Edwards, Author at The South African.

Top quality Maths and Science teaching is now available to South African students, offline through their cell phones.

Photo: Youtube / The Paper Video Subject Maps

For the first time, no matter where a student lives or what their economic situation may be, Grade 8 and 9 students can turn their cell phone into an experienced teacher in key subjects – without the internet or data.

This innovation, called Subject Maps has come about through a partnership of Paper Video and the Actuarial Society of South Africa (ASSA). It is a response to the dire state of Maths and Science education in SA, particularly in under-resourced schools.

It works by allowing students access through their own cell phones to professional video lessons for every topic in Mathematics, Physics, Chemistry, Biology and Accounting in Grades 8 and 9...

Check out the video that explains the concept below:


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Source: The South African