Recent advances in deep learning and exponential growth in the use of machine learning across application domains have made AI acceleration critically important. IBM Research has been building a pipeline of AI hardware accelerators to meet this need. At the 2018 VLSI Circuits Symposium, we presented a multi-TeraOPS accelerator core building block that can be scaled across a broad range of AI hardware systems. This digital AI core features a parallel architecture that ensures very high utilization and efficient compute engines that carefully leverage reduced precision.

Machine learning platforms are not the wave of the future. Developers need to know how and when to harness their power. Working within the ML landscape while using the right tools like Filestack can make it easier for developers to create a productive algorithm that taps into its power. The following machine learning platforms and tools -- listed in no certain order -- are available now as resources to seamlessly integrate the power of ML into daily tasks. H2O was designed for the Python, R, and Java programming languages by H2O.ai. By using these familiar languages, this open source software makes it easy for developers to apply both predictive analytics and machine learning to a variety of situations.

AI or Artificial Intelligence, is impacting the daily life of every human, and this impact is increasing at a rapid rate. The perfect example is how a Japanese farmer Makoto Koike used Google's popular open-source machine learning framework and developed a deep learning model by which he was able to sort out the cucumbers by their shape, size, and other attributes. Though this system isn't perfect it is surely a sign about AI is able to transform even the smallest family-run businesses. IT giants like Google, Microsoft, Amazon, Apple, and Facebook have realised the importance of AI and its transformative power. Some of the best examples of AI are Google's search and translation tools that are widely used across the globe and Amazon's recommendation system.

Even the current generation of relatively basic machine-learning algorithms can display behavior akin to that of a human with cognitive issues, said Vahid Behzadan, a PhD candidate at Kansas State University and a co-author of the paper. Further down the line: More developed AIs might be more susceptible to more complex disorders, some experts say. But, but, but: These latter possibilities are just thought experiments given where AI is today. In the meantime, DeepMind, the British AI company that Google acquired in 2014, has applied psychological tools to analyze how a neural network solved basic image-classification tasks. The big picture: AI psychologists -- whether humans or specialized algorithms -- may one day be needed to probe artificial brains the way human psychologists try to understand ours. The benefits may not be limited to helping AI behave, Behzadan said: "This research can provide a deeper insight into human psychology and human psychopathology."

Researchers from Google's DeepMind subsidiary have developed deep neural networks that "have a remarkable capacity to understand a scene, represent it in a compact format, and then'imagine' what the same scene would look like from a perspective the network hasn't seen before," writes Timothy B. Lee via Ars Technica. From the report: A DeepMind team led by Ali Eslami and Danilo Rezende has developed software based on deep neural networks with these same capabilities -- at least for simplified geometric scenes. Given a handful of "snapshots" of a virtual scene, the software -- known as a generative query network (GQN) -- uses a neural network to build a compact mathematical representation of that scene. It then uses that representation to render images of the room from new perspectives -- perspectives the network hasn't seen before. Under the hood, the GQN is really two different deep neural networks connected together.

Artificial-intelligence (AI) research covers a number of topics, including machine learning (ML). ML covers a lot of ground as well, from rule-based expert systems to the latest hot trend--neural networks. Neural networks are changing how developers solve problems, whether it be self-driving cars or the industrial Internet of Things (IIoT). Neural networks come in many forms, but deep neural networks (DNNs) are the most important at this point. A DNN consists of multiple layers, including input and output layers plus multiple hidden layers (Figure 1).

Today I am tremendously excited to announce IBM Cognitive Systems' newest applied artificial intelligence (AI) offering, PowerAI Enterprise. This new platform extends all the capability we have been packing into our distribution of deep learning and machine learning frameworks, PowerAI, by adding tools which span the entire model development workflow. With these capabilities, customers are positioned to develop better models more quickly, and as their requirements grow efficiently scale and share data science infrastructure. All machine learning and deep learning models train on large amounts of data. Fortunately (and unfortunately), organizations are swimming in data sitting in structured and unstructured forms, and beyond the data they have under their control, organizations also have access to data for free or for a fee from a variety of sources.

Children who have autism often find it challenging to ascertain the emotional state of people surrounding them. For example, they have trouble differentiating between a scared and a happy face. In order to resolve this concerning issue, some therapists have begun employing children-friendly robots who demonstrate these emotions and help them imitate these feelings so that they are then able to respond to them appropriately. These robots are designed in a way that they engage autistic kids in a personalized way. However, this therapy can work only if a robot can accurately comprehend a child's behavior and analyze his/her level of focus and excitement during the course of therapy. This is where the researchers from MIT Media Lab come into the picture!

On Monday, non-profit AI research company OpenAI published a blog post about OpenAI Five, a group of five neural networks designed to work as a team while playing the real-time computer strategy game called Dota 2. According to the post, OpenAI Five can now beat a team of five human amateur players at the game, albeit with specific restrictions placed on gameplay. In August, it will attempt to beat a team of professional Dota 2 players at The International (TI), an annual Dota 2 tournament hosted by the game's developer, Valve Corporation. In Dota 2, two teams of five players battle to destroy the other team's "Ancient," a structure at the center of their base. Each player controls a different character, known as a "hero." These heroes have their own abilities, strengths, and weaknesses, and a team's ability to cooperate is key to its success.

Building high-quality artificial intelligence (AI) is hard work. It's a specialized discipline that historically has required highly skilled specialists, aka data scientists. Any time you require some highly skilled, highly paid practitioner to accomplish something of value, you've introduced a bottleneck into that process. That explains why there's been such a huge push for machine learning (ML) automation. It also explains why many organizations are seeking to democratize these functions to less skilled personnel.