Artificial Intelligence (AI) and Machine Learning (ML) are some of the hottest topics right now. The term "AI" is thrown around casually every day. You hear aspiring developers saying they want to learn AI. You also hear executives saying they want to implement AI in their services. But quite often, many of these people don't understand what AI is.

NEW YORK; Nov. 14, 2017 – Accenture (NYSE: ACN) has launched the Accenture Future Talent Platform, an interactive learning platform that includes talent transformation services to help clients develop their IT workforces in critical areas such as digital, cloud, security and artificial intelligence. Accenture has already used the platform internally to train more than 180,000 of its people globally in the latest digital technologies – or New IT – in just over 20 months. Now, through its technology consulting and talent and organization practices, Accenture is bringing these learning capabilities to clients to help them run agile, intelligent businesses. "With technology accelerating at a breathtaking pace, companies need to upskill their people at greater speed and scale to avoid being disrupted by competitors," said Bhaskar Ghosh, group chief executive of Accenture Technology Services. "Accomplishing this requires creating a culture of continuous learning that empowers people to build new skills. The Accenture Future Talent Platform helps companies do exactly that through a unique, interactive experience and rich curriculum."

I recently completed all available material (as of October 25, 2017) for Andrew Ng's new deep learning course on Coursera. I found all 3 courses extremely useful and learned an incredible amount of practical knowledge from the instructor, Andrew Ng. Ng does an excellent job of filtering out the buzzwords and explaining the concepts in a clear and concise manner. For example, Ng makes it clear that supervised deep learning is nothing more than a multidimensional curve fitting procedure and that any other representational understandings, such as the common reference to the human biological nervous system, are loose at best. The specialization only requires basic linear algebra knowledge and basic programming knowledge in Python.

We address the problem of bootstrapping language acquisition for an artificial system similarly to what is observed in experiments with human infants. Our method works by associating meanings to words in manipulation tasks, as a robot interacts with objects and listens to verbal descriptions of the interactions. The model is based on an affordance network, i.e., a mapping between robot actions, robot perceptions, and the perceived effects of these actions upon objects. We extend the affordance model to incorporate spoken words, which allows us to ground the verbal symbols to the execution of actions and the perception of the environment. The model takes verbal descriptions of a task as the input and uses temporal co-occurrence to create links between speech utterances and the involved objects, actions, and effects. We show that the robot is able form useful word-to-meaning associations, even without considering grammatical structure in the learning process and in the presence of recognition errors. These word-to-meaning associations are embedded in the robot's own understanding of its actions. Thus, they can be directly used to instruct the robot to perform tasks and also allow to incorporate context in the speech recognition task. We believe that the encouraging results with our approach may afford robots with a capacity to acquire language descriptors in their operation's environment as well as to shed some light as to how this challenging process develops with human infants.

A Google doodle from earlier this year commemorated the 100th anniversary of the Silent Parade, during which almost 10,000 African-Americans marched in New York City to protest violence against African-Americans. A Google doodle from earlier this year commemorated the 100th anniversary of the Silent Parade, during which almost 10,000 African-Americans marched in New York City to protest violence against African-Americans. Chances are you've pulled up the Google search page, surprised and perhaps delighted to find the usual blue, red, yellow and green letters transformed to make the Google logo into a colorful cartoonish image to celebrate an important anniversary or holiday. Google has been sharing its beloved Google doodles with millions of people around the world since 2000. The idea for doodles came in 1998 after Google founders Larry Page and Sergey Brin added a stick figure man to the search engine's logo.

Artificial intelligence is a term that comes with a lot of baggage, thanks to popular culture. From Asimov to Westworld, machines that act and think like humans are a mainstay in science fiction. In reality, however, there are limits to what artificial intelligence can do: machines don't make good decisions on their own, and they're not creative. Examples of the limitations of AI abound: Last year, for example, trolls corrupted Tay, Microsoft's Twitter bot, so badly she had to be taken offline. This month an AI is trying (and failing) to write the first sentence of a novel.

Many years ago, when I was a computer science student, I was impressed by -and consequently interested in- Neural Networks. At that time Machine Learning was not the buzz word that is today (it was called "Conectionism") and I had the chance to migrate the Rochester Connectionist Simulator to windows (from *nix) for the great joy of my professor then. But as you may know the time was not yet right and Artificial Neural Networks (abbreviated as ANN) was dropped in the "nice to have...some day" list, waiting for a better moment. If you are reading this, it means that you know that ANN have arrived and are here to stay and as I am currently working in a company where data is an asset and applying Machine Learning (ML) is one of the current paths to data monetization, I decided I should dust off my old books and also learn about the new trends in IA (I guess I don't need to explain this acronym). I searched for a good online course and signed in for Kirill Emerenko's excellent course "Machine Learning A-Z". There is a lot of information in that course and as I was going through the different sections I realized that I would have a hard time remembering everything, so I decided to make a mind map.

From time to time, the Singularity Hub editorial team unearths a gem from the archives and wants to share it all over again. It's usually a piece that was popular back then and we think is still relevant now. This is one of those articles. It was originally published October 19, 2016. We hope you enjoy it!

Cray Inc. (Nasdaq:CRAY) announced a comprehensive set of Artificial Intelligence (AI) products and programs that will empower customers to learn, start, and scale their deep learning initiatives. As AI and deep learning continue to transform entire industries and scientific disciplines, Cray is leveraging its supercomputing expertise, technologies, and best practices to advance the adoption of deep learning. An AI collaboration agreement with Intel, leveraging Intel's AI technologies to advance the state-of-the-art in distributed deep learning and machine learning. Cray is committed to working closely with our customers, partners, and innovators in AI to drive the adoption of deep learning in science and enterprise," said Fred Kohout, Cray's senior vice president of products and chief marketing officer. "At Cray, we are bringing together a powerful set of innovative systems, software, deep learning architectures, and a hands-on lab environment to give organizations a trusted partner ...

Dell EMC announced the launch of its new machine learning and deep learning solutions, which according to the company is in line with it continuing its work to bring high-performance computing (HPC) and data analytics capabilities to mainstream enterprises worldwide. Dell EMC believes that this enables organisations to take advantage of the convergence of HPC and data analytics and realise advancements in areas including fraud detection, image processing, financial investment analysis and personalised medicine. According to the company, these new innovations represent the next step in the company's focus on democratising HPC, optimising data analytics with artificial intelligence (AI) technology innovations, and advancing both the HPC and AI communities. While AI techniques, such as machine learning and deep learning, being rapidly being deployed by many organisations across several industries, only a small number possess the expertise to design, deploy and manage such systems to use them effectively for rapidly gaining new insights. Dell EMC believes that by leveraging Dell's ecosystem of partnerships and internal expertise in HPC and data analytics services, the company's new solutions offer customers the ability to harness the power of the massive amounts of their collected data, delivering faster, better and deeper business insights in real-time.