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The missing part of the Machine Learning revolution

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There's no doubt that we're entering the age of AI, with Machine Learning touching almost everything we're involved in on a day-to-day basis. Spurred on by step innovations in data storage and computing power, Neural Nets are back from the 70's with a bang. Medicine, security, customer service, fraud detection, you name it -- there are well funded companies applying Machine Learning to improve and augment it. Heck, you might have even found this post through Medium's Machine Learning-based recommender systems. Deep Learning, for whatever reason, seems to work really well for a number of problems with immediate impact.


Wanted: AI That Can Spy

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The deluge of satellite imagery leaves U.S. intelligence agencies with the world's biggest case of FOMO--"fear of missing out"--because human analysts can sift through only so many images to spot a new nuclear enrichment facility or missiles being trucked to different locations. That's why U.S. intelligence officials have sponsored an artificial-intelligence challenge to automatically identify objects of interest in satellite images. Since July, competitors have trained machine-learning algorithms on one of the world's largest publicly available data sets of satellite imagery--containing 1 million labeled objects, such as buildings and facilities. The data is provided by the U.S. Intelligence Advanced Research Projects Activity (IARPA). The 10 finalists will see their AI algorithms scored against a hidden data set of satellite imagery when the challenge closes at the end of December.


The Growth And Future Of Algorithmic Trading

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Looking back to the primitive times, when fire was the greatest achievement of mankind, who could've thought in their wildest dreams about what we as humans have achieved today? Look at the speed with which information can travel across the planet. Today, Algorithmic trading is amongst the most talked about technologies in the recent years. It has given trading Firms more power in the rapidly evolving markets by eliminating human errors and changing the way Financial markets are interlinked today. We are curious to know many other factors pertaining to the subject.


discussing the changing landscape of artificial intelligence

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Artificial intelligence isn't about to pick up the laundry and drive the car in for repairs, but it is shifting the goalposts for contact centres thanks to incredible growth in chatbot availability and capability. AI and the solutions it enhances has become the gold rush of the Western world – a digital hunt for the customer experience oil that sits on the frontlines of chatbot capability and evolution. Amazon has released its increasingly impressive Alexa and Echo range of products, Microsoft has created a voice-activated speaker in partnership with Harmon Kardon, and Google has showcased such impressive advancements in AI that everyone has stopped to play with whatever it does next. The statistics are as exciting as the inventions. According to Juniper Research, chatbots can potentially save the business as much as $US 8 billion by 2020.


Chatbot Best Practices - Making Sure Your Bot Plays Well With Users

@machinelearnbot

Summary: This is the third in our series on chatbots. In this installment we'll look at the best practice dos and don'ts as described by a number of successful chatbot developers. In our first article we covered the chatbot basics including their brief technological history, uses, basic design choices, and where deep learning comes into play. The second article focused on the universal NLU front ends for all chatbots and some of the technical definitions and programming particulars necessary to understand how these really function. In this article, we've scoured the internet for advice from successful chatbot developers to provide some useful best practices, or at least some valuable dos and don'ts.


Q&A: Famed economist Henry Kaufman says robots are 'greatest challenge' to workers

USATODAY

The S&P 500 is up 21% since Election Day. Henry Kaufman, 90, the renowned economist, former managing director at Wall Street firm Salomon Brothers and author of Tectonic Shifts In Financial Markets, shared his views with USA TODAY on the future of the American worker, tax cuts and the middle class, the retirement savings crisis and the risks facing computer-driven markets. Kaufman is president of Henry Kaufman & Company, an economic and financial consulting firm established in 1988. USA TODAY: Robots are invading the workplace. Is technology a threat to middle-class workers? KAUFMAN: The greatest challenge that workers face and we as a society face is that labor over a longer period of time will become more and more obsolete.


Artificial Intelligence Can Hunt Down Missile Sites in China Hundreds of Times Faster Than Humans

WIRED

Intelligence agencies have a limited number of trained human analysts looking for undeclared nuclear facilities, or secret military sites, hidden among terabytes of satellite images. But the same sort of deep learning artificial intelligence that enables Google and Facebook to automatically filter images of human faces and cats could also prove invaluable in the world of spy versus spy. An early example: US researchers have trained deep learning algorithms to identify Chinese surface-to-air missile sites--hundreds of times faster than their human counterparts. The deep learning algorithms proved capable of helping people with no prior imagery analysis experience find surface-to-air missile sites scattered across nearly 90,000 square kilometers of southeastern China. Such AI based on neural networks--layers of artificial neuron capable of filtering and learning from huge amounts of data--matched the overall 90 percent accuracy of expert human imagery analysts in locating the missile sites.


Diary of a Data Scientist at Booking.com – Towards Data Science

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I joined Booking.com as a data scientist about two and a half years back, straight after a 3 years consulting gig in Dubai. Moving from consulting to a pure data science role was a big shift in my career and in hindsight I'm very happy I made that choice. In fact, I was already impressed with the company during my interviews. What I liked the most was that I was interviewed by peers who were already in the same role, which allowed for many'quality' interactions during the process and reaffirmed the recruiter's claim that the company had a'flat hierarchy'. Also the background of the interviewers was very diverse and interesting -- one had a PhD in astronomy, and the other was CTO of his own startup.


Here's what it takes to make IoT data ready for AI and machine learning

@machinelearnbot

The integration of artificial intelligence and the Internet of Things introduces a wide array of connected health tools that produce a vast amount of data that must be synthesized, analyzed, stored and communicated by a robust information infrastructure. But if hospitals don't structure and store IoT patient data properly, that information could be rendered not assessable by AI tools. For starters, significant infrastructure is needed to streamline IoT-generated data to make sure it is simple to assess and manage with AI. "AI adoption and scale will be accelerated by the relatively low cost of deployment," said Rick Krohn, president of HealthSense, a connected health consulting firm. "A terabyte of storage costs less than $100, and wearable sensors and cloud infrastructure are becoming increasingly affordable. But AI requires sophisticated applications that deliver contextually aware right-place-right-time clinical decision support."


Artificial Intelligence and Recruitment. Threat or Opportunity?

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There has been a lot of press coverage over the summer about robots and more generally Artificial Intelligence (AI) taking over the world of work. BBC news has recently run a special feature on it which included an interactive test to check if your job is at risk (Will a robot take your job?). Artificial Intelligence is the science of how to make machines that can think for themselves according to Stanford University. Increasingly, machines have been able to learn and improve their own performance to produce results that until recently was only thought possible to obtain using human intelligence and social experience. It is this achievement which has thrust AI as a concept back into the mainstream press, along with all the sensational headlines.