Human Interpretable Machine Learning (Part 1) -- The Need and Importance of Model Interpretation


The field of Machine Learning has gone through some phenomenal changes over the last decade. Starting off as just a pure academic and research-oriented domain, we have seen widespread industry adoption across diverse domains including retail, technology, healthcare, science and many more. Rather than just running lab experiments to publish a research paper, the key objective of data science and machine learning in the 21st century has changed to tackling and solving real-world problems, automating complex tasks and making our life easier and better. More than often, the standard toolbox of machine learning, statistical or deep learning models remain the same. New models do come into existence like Capsule Networks, but industry adoption of the same usually takes several years.

The Impact and Challenges of Artificial Intelligence for Next-Gen Enterprises


Artificial Intelligence (AI) is not a new phenomenon. It continues to develop and its applications are already very present in our personal daily life (gaming, robotics, connected objects …), arousing as much enthusiasm as fear. This complex concept gained its success in the science fiction world. What can be the role of artificial intelligence in the enterprise of the future? Will AI make organizations smarter?

Basic instincts


Some say artificial intelligence needs to learn like a child. Babies are born with instincts that help us learn common sense, so far elusive for AI algorithms. It's a Saturday morning in February, and Chloe, a curious 3-year-old in a striped shirt and leggings, is exploring the possibilities of a new toy. Her father, Gary Marcus, a developmental cognitive scientist at New York University (NYU) in New York City, has brought home some strips of tape designed to adhere Lego bricks to surfaces. Chloe, well-versed in Lego, is intrigued. But she has always built upward. Could she use the tape to build sideways or upside down?

CMO playbook: How Pinterest uses data to create great customer experiences


Because customer experience is genuinely important, it has become one of the great buzzwords of our time. Along with stalwart mediaspeak terms like digital transformation and artificial intelligence, customer experience occupies a hallowed place in the modern jargon hall of fame. Creating a great experience is dang hard because it demands that we rethink all the touchpoints and interactions a customer has with our company, brand, reputation, products, and services. Although we can control some of these interaction points - for example, the products we release - we can only influence other factors, such as brand reputation or how customers talk about us. However, despite the difficulty, creating positive customer experiences is superlatively important for every business today. For this reason, I invited a major customer experience practitioner to be a guest on episode 290 of the CXOTalk series of conversations with the world's top innovators.

Alexa's recording snafu was improbable, but inevitable


Amazon's Alexa recently made headlines for one of the strangest consumer AI mistakes we've ever heard of: A family in Portland, Oregon claims that the company's virtual assistant recorded a conversation and sent it to a seemingly random person in the husband's contact list. Alexa didn't just make one slip-up -- it made several that, when combined, led to a pretty remarkable breach of privacy. The company's explanation, provided to news outlets yesterday, makes clear just how unlikely this whole situation was: "Echo woke up due to a word in background conversation sounding like'Alexa,'" the statement reads. "Then, the subsequent conversation was heard as a "send message" request. At which point, Alexa said out loud "To whom?"

Video: Andrew Ng on Deploying Machine Learning in the Enterprise - insideHPC


In this video from Intel AI DevCon 2018, Andrew Ng from and When you ask Siri for directions, peruse Netflix's recommendations or get a fraud alert from your bank, these interactions are led by computer systems using large amounts of data to predict your needs. The market is only going to grow. By 2020, the research firm IDC predicts that AI will help drive worldwide revenues to over $47 billion, up from $8 billion in 2016. Still, Andrew NG says fears that AI will replace humans are misplaced: "Despite all the hype and excitement about AI, it's still extremely limited today relative to what human intelligence is."

How is artificial intelligence changing science?


Intel's Gadi Singer believes his most important challenge is his latest: using artificial intelligence (AI) to reshape scientific exploration. In a Q&A timed with the first Intel AI DevCon event, the Intel vice president and architecture general manager for its Artificial Intelligence Products Group discussed his role at the intersection of science--computing's most demanding customer--and AI, how scientists should approach AI and why it is the most dynamic and exciting opportunity he has faced. How is AI changing science? Scientific exploration is going through a transition that, in the last 100 years, might only be compared to what happened in the '50s and '60s, moving to data and large data systems. In the '60s, the amount of data being gathered was so large that the frontrunners were not those with the finest instruments, but rather those able to analyze the data that was gathered in any scientific area, whether it was climate, seismology, biology, pharmaceuticals, the exploration of new medicine, and so on.



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How Artificial Intelligence Will Shape The Mobile App Development


Mobility, as well as app ecosystem, have made remarkable changes in our daily lives. I believe that it has transformed the way we deal with our activities whether it is online shopping, entertainment etc. The emergence of Artificial Intelligence is equally having a greater impact on our interactions with our gadgets along with mobile apps. AI is now multi-faceted with a wide range of impacts, especially in mobile app solutions. From deep learning, advanced machine learning to natural language processing as well as advanced algorithms, AI currently works at various levels.

Why emotionally intelligent machines are the future of AI


When we think about artificial intelligence, it's usually something involving robot overloads and the answer to how many tablespoons are in a cup. Less often do we think about their emotional intelligence, something so intrinsic to human interaction that we take it for granted. Pamela Pavliscak, CEO of Change Sciences to took the stage at TNW Conference 2018 to discuss the "emotion revolution" we'll see in our machines and software within the next years. For AI and virtual assistants to make the most positive impact, they'll need to know how to understand and behave within the framework of our very human emotional cues. This concept isn't new; technology companies have tried to humanize technology for ages, to varying degrees of success.