Vega 7nm or Vega 20 as AMD used to call this GPU on its roadmap a while ago, naturally increased the performance in deep learning and artificial intelligence but at the same time adding 32GB of HBM 2 made it too expensive to launch as a gaming part. This is a "we told you so" moment. The original Vega 64 and 56 only made profit as the market went crazy for any hardware suitable for cryptocurrency mining. Since this interest wave seems to be over, AMD could not bet on making let's say, a 16GB HBM 2 Vega 7nm gaming part, as the BOM would still be too expensive. David Wang SVP of Engineering at AMD, an ex-Synaptics chap who took over after Raja left has, went into a few more details about Vega 7nm.

The two major tasks in medical imaging that appear to be naturally predestined to be solved with AI algorithms are segmentation and classification. Most of techniques used in medical imaging were conventional image processing, or more widely formulated computer vision algorithms. One can find many works with artificial neural networks, the backbone of deep learning. However, most works were focused on conventional computer vision which focused, and still does, on "handcrafted" features, techniques that were the results of manual design to extract useful and differentiating information from medical images. Some progress was visible in the late 90s and early 2000s (for instance, the SIFT method in 1999, or visual dictionaries in early 2000s) but there were no breakthroughs.

Deep learning has been popular in artificial intelligence with many applications due to great successes in many perceptual tasks (e.g., object detection, image understanding, and speech recognition). Moreover, deep learning is also critical in data science, especially for big data analytics relying on extracting high-level and complex abstractions as data representations based on a hierarchical learning process. In realizing deep learning, supervised and unsupervised approaches for training deep architectures have been empirically investigated based on the adoption of parallel computing facilities such as GPUs or CPU clusters. However, there is still limited understanding of why deep architectures work so well and how to design computationally efficient training algorithms and hardware acceleration techniques. At the same time, the number of end devices, such as IoT (Internet of Things) devices, has dramatically increased.

The workflow for building machine learning models often ends at the evaluation stage: you have achieved an acceptable accuracy, and "ta-da! Beyond that, it might just be sufficient to get those nice-looking graphs for your paper or for your internal documentation. In fact, going the extra mile to put your model into production is not always needed. And even when it is, this task is delegated to a system administrator. However, nowadays, many researchers/engineers find themselves responsible for handling the complete flow from conceiving the models to serving them to the outside world.

Semiconductor Engineering sat down with Rob Aitken, an Arm fellow; Raik Brinkmann, CEO of OneSpin Solutions; Patrick Soheili, vice president of business and corporate development at eSilicon; and Chris Rowen, CEO of Babblelabs. What follows are excerpts of that conversation. SE: Where are we with machine learning? What problems still have to be resolved? Aitken: We're in a state where things are changing so rapidly that it's really hard to keep up with where we are at any given instance. We've seen that machine learning has been able to take some of the things we used to think were very complicated and rendered them simple to do.

Substantial research work at our high-tech research center followed by relevant POCs and real world applications have reinforced our capabilities to produce actual Artificial Intelligence for the applications that changes the working patterns of the modern day industries.

The third way to get involved with OpenAI is as an OpenAI Scholar. Under this program OpenAI is providing 6-10 stipends and mentorship to individuals from underrepresented groups to study deep learning full-time for 3 months and open-source a project. This is a remote program and is open to anyone with US work authorization located in US timezones. In return, scholars are asked to document their experiences of studying deep learning and hopefully inspire others to do the same.

IBM on Friday unveiled its next-generation Power Systems Servers that includes its new processor that will work for artificial intelligence systems that require heavy computing capability. "Built specifically for compute-intensive AI workloads, the new POWER9 systems are capable of improving the training times of deep learning frameworks by nearly four times, allowing enterprises to build more accurate AI applications, faster," IBM said in a statement. With a focus on AI and machine learning, it can do a lot of tasks with greater efficiency. As a result of using this new, high-powered system, data scientists can build applications faster, ranging from deep learning insights in scientific research, real-time fraud detection and credit risk analysis. "IT infrastructure needs to be re-designed for the AI era, which lets companies analyse data in milliseconds and make decisions driven by data. AI workloads demand new hardware and software paradigms and the infrastructure to deliver data-driven workloads," said Viswanath Ramaswamy, Director – Systems, India/South Asia.

Attacks are getting more complex. This is especially true when it comes to cyberwar, so much so that government sponsored attacks have been bolstered by research investments that approach military proportions. Just look at the recent report published by the US State Department, which said that strategies for stopping cyber attacks need to be fundamentally reconsidered in light of complex cyber threats posed by rival states. In order to detect and stop these attacks, innovation is required. I say that because anomaly detection based on traditional correlation rules often results in too many false positives and events that can reasonably be manually reviewed.

Scientists at the Massachusetts Institute of Technology unveiled the first artificial intelligence algorithm trained to be a psychopath. The AI was fittingly dubbed "Norman" after Norman Bates, the notorious killer in Alfred Hitchcock's Psycho. MIT scientists Pinar Yanardag, Manuel Cebrian and Iyad Rahwan trained Norman to perform image captioning, "a deep learning method" that allows AI to generate text descriptions for images. However, the team exclusively exposed Norman to violent and disturbing images posted on a subreddit dedicated to death. They then gave Norman a Rorschach inkblot test and the AI responded with chilling interpretations such as, "a man is electrocuted and catches to death," "pregnant woman falls at construction" and "man is shot dead in front of his screaming wife."