Hewlett Packard Enterprise (HPE) today announced a new tool that combines machine learning APIs and cognitive services with visual development to democratize those cutting-edge technologies for mainstream developers. The new offering is called HPE Haven OnDemand Combinations and, as the name suggests, it's built upon the existing HPE Haven OnDemand, a cloud platform used for Big Data development that provides machine learning APIs and services. HPE described HPE Haven OnDemand Combinations -- available now upon request -- as the quickest way to add intelligence to apps, as it reportedly smoothes the often tedious process developers and data scientists have to go through to leverage existing data-related APIs in their software development. "The new offering provides a pre-built catalogue of cognitive services and an intuitive drag-and-drop interface," HPE said in a statement today. "Developers can chain together multiple machine learning APIs into combinations and copy and paste the code directly into their development projects, to quickly and easily create breakthrough mobile and enterprise applications."

Alan Turing was a multi-talented British mathematician who helped to both win the Second World War and invent the earliest computers, both while leading the Allied code-breaking efforts at Blechley Park. However, this impact on history may have been even greater through his academic work; his seminal paper On Computable Numbers laid down the foundations for modern computer theory, and his thinking on artificial intelligence is still some of the most influential today. He devised the famous Turing Test for true AI: if an AI can endure a detailed, text-based interrogation by a human tester or testers, and those testers cannot accurately determine whether they are speaking to a human or a robot, then true artificial intelligence has been achieved. With all we now know about the ability of neural networks to find patterns in behavior, this does seem like a somewhat low bar to consciousness -- but it's easy to remember, historically important, and it has alliteration, which means it's famous.

The complexity in languages arises from ambiguities, structure, context and domain dependencies, and word ordering. As discussed in our previous blog, machines and humans learn language. Natural Language Processing (NLP) is the branch of AI that enables effective communication by obtaining useful information from computing system. The goal of NLP is to enhance communication by extracting semantic and pragmatic meaning from human interactions. The presence of NLP to assist our text composition has been around for a while.We are familiar with spell-checks and grammatical corrections as a useful feature of Word processing software like Microsoft Word.Such NLP technology to process and construct our text is now universally implemented in most web-platforms and mobile devices to assist our language inputs. Enormous amount of data is generated every day from our daily business activities and online-interactions.

Electronics giant Flextronics, through its solar gear subsidiary NEXTracker, has acquired a young startup called BrightBox Technologies, which builds predictive modeling and machine learning software, the companies announced late on Monday. NEXTracker makes hardware, called trackers, which automatically tilt solar panels throughout the day to face the sun to increase the amount of power that panels generate. These trackers are generally used on large solar panel farms in remote locations that sell their power to utilities or large companies. Flextronics (now called Flex) itself bought NEXTracker last year for 330 million. Now, NEXTracker is acquiring BrightBox Technologies, a three-year-old company based in Berkeley, Calif. that has developed software to optimize heating and cooling systems in buildings.

I'm working my way through the Coursera course on Machine learning by Andrew Ng, and I'm really confused about the bias unit? In the programming assignment, we always set the parameter for the bias unit to 0, so how does it affect the neural network in any way? Since the bias unit will be multiplied by it's parameter/weight (which seems to always be zero), wouldn't it be completely insignificant?

Artificial intelligence is going to have a massive impact on multiple business verticals over time. The displacement of both blue collar and white collar work by machine learning is going to cause major societal displacements in the next 10-20 years[0]. While there is a lot of discussion in the popular press about general purpose AI (aka AGI - which is defined as a machine that can perform any intellectual task a person can), much less emphasis has been placed on near-term specific vertical markets or areas that AI and machine learning (ML) are likely to transform in the coming 5 years. In short, I think AGI is still 10 years away, but vertical products driven by AI will be transformative in the coming years. The areas listed below are underinvested by entrepreneurs and VCs.

Google DeepMind is launching a project to reduce the time it takes doctors to prepare treatment for head and neck cancers. Alphabet's London-based artificial intelligence division has partnered with the UK's National Health Service and will be conducting the research in coordination with the University College London Hospital. Head and neck cancers are hard to plan treatment for because of their close proximity to important parts of the body. Before any kind of radiation treatment, clinicians will prepare a detailed map of where radiation will be administered on a patient in order to avoid damaging surrounding tissue. DeepMind says planning can take doctors up to four hours for head and neck cancers, and it hopes that by applying machine learning it will be able to automate parts of the process and reduce that planning time down to an hour.

Nvidia has been a leader in producing the technology behind high-quality graphics for years, but the company is now betting on a different future. With the rapid advances in self-driving vehicles, warehouse robots, diagnostic assistants, and speech and facial recognition, there's plenty of reasons for companies to be excited about deep-learning-based artificial intelligence (AI). Nvidia is one such technology firm that is extremely bullish in this space. In a recent conversation with CXOtoday, Vishal Dhupar, Managing Director-South Asia, Nvidia said that the company is moving ahead with its AI-focused hardware, software, and solutions for the enterprise. As such data scientists in both industry and academia have been using graphics processing units (GPUs) for machine learning to make groundbreaking innovations across a variety of applications including image classification, video analytics, speech recognition and natural language processing or NLP. In particular, Deep Learning, the use of sophisticated, multi-level "deep" neural networks to create systems that can perform feature detection from massive amounts of unlabeled training data, is an area where Nvidia is significantly investing in recent quarters.

Artificial intelligence has reached yet another milestone, of sorts, in its development: Elon Musk's OpenAI group has decided to hire "techie cops." These cops aren't futuristic vigilantes out to destroy a rogue AI program lest it kick-start the Terminator saga. Instead, they're looking for those who would abuse AI for illegal or unethical purposes. But even these kinds of policing efforts don't help assuage all the fears of people who are still wary of the very concept of artificial intelligence. It will take transparency and trust to change minds about what is, essentially, our inevitable future.

In a report commissioned by the Pentagon's Defense Science Board, top defense and technical professionals argue that the United States needs to prepare for artificial intelligence in warfare – because other countries might prepare for it first. "While evident that the DoD is moving forward in the employment of autonomous functionality, it is equally evident that the pull from diverse global markets is accelerating the underlying tech base and delivering high-value capabilities at a much more rapid pace," the introduction to the 121-page report reads. Along with this comes a caveat that the study isn't recommending major projects, "given the current budget environment." Instead, it suggested a wide variety of experimental projects, plus recommending that the DoD reach out to private "non-traditional R&D communities." The report operated under the idea that autonomous weapons capabilities were going to be developed eventually, and that the United States needs to avoid an AI Cold War.