Today we're excited to announce the availability of NVIDIA DIGITS 4. DIGITS 4 introduces a new object detection workflow and DetectNet, a new deep neural network for object detection that enables data scientists and researchers to train models that can detect instances of faces, pedestrians, traffic signs, vehicles and other objects in images. Object detection is one of the most challenging problems in computer vision and is the first step in several computer vision applications. The goal of an object detection system is to detect all instances of objects of a known category in an image. Figure 1 shows the final results of an object detection system trained with DIGITS which can detect vehicles on a construction site. Starting with a successful vehicle detection system like this, you can solve a number of other problems such as recognizing the makes and models of the vehicles, counting and tracking vehicle locations over time, generating natural language descriptions of the images and so on.

Kyulux Inc announced that it has a license with Harvard University's Molecular Space Shuttle deep learning system to develop new display and lighting application materials, according to a news release. Kyulux is an advanced materials start-up company that commercializes thermally activated delayed fluorescence (TADF) OLED display and lighting technology. The Molecular Space Shuttle is an artificial intelligence platform designed by Alán Aspuru-Guzik's group at Harvard's chemistry and chemical biology department, where Aspuru-Guzik is a professor.

FANUC, the world's largest maker of industrial robots, plans to start connecting 400,000 of their installed systems by the end of this year. The goal is to collect data about their operations and, through the use of deep learning, improve performance. Similarly, Kuka is building a deep-learning AI network for their industrial robots. FANUC is now moving forward to connect all its manufacturing robots. The system proactively detects and informs of a potential equipment or process problem before unexpected downtime occurs.

When it comes to artificial intelligence, people typically envision a Sci-Fi world where robots take over humanity as we know it. But artificial intelligence is already here, improving everyday technologies such as ecommerce, surveillance systems and many others. To shed some light on how AI is used in this industry, we've asked Cristina Vatamanu, malware researcher at Bitdefender's Antimalware Labs, to answer a few questions. For the past 6 years, Cristina has demonstrated strong expertise in reverse engineering, exploit analysis, threat analysis and automated systems. She is now pursuing a PhD in Machine Learning theory in malware detection systems at "Gheorghe Asachi" Technical University in Iasi.

"In order for a program to improve itself substantially it would have to have at least a rudimentary understanding of its own problem-solving process and some ability to recognize an improvement when it found one. There is no inherent reason why this should be impossible for a machine. Given a model of its own workings, it could use its problem-solving power to work on the problem of self-improvement. The present programs are not quite smart enough for this purpose; they can only deal with the improvement of programs much simpler than themselves. Once we have devised programs with a genuine capacity for self-improvement a rapid evolutionary process will begin.... Whether or not we could retain some sort of control of the machines, assuming that we would want to, the nature of our activities and aspirations would be changed utterly by the presence on earth of intellectually superior beings. "The audience is tense with excitement as the hero in the film play struggles frantically with the control apparatus of a submarine that is fast sinking to the ocean bottom.

Bill Gates also weighed in, calling intelligent machines dangerous and implying that they may be more trouble than they're worth. Instead, we should feel proud of and satisfied by the enormous progress that we've achieved in AI thus far, especially if we think of the incremental improvements that we'll see in information management based on automatic understanding in even just the next 10 years. Bio: Marco Varone, founder, president and CTO of Expert System, is one of the leading experts on semantic technology and natural language processing. He has worked in the field application of semantic technology in every advanced context: search engines, text analytics, natural language interfaces, Q & A systems, automatic categorization and many others.

Bill Gates also weighed in, calling intelligent machines dangerous and implying that they may be more trouble than they're worth. Instead, we should feel proud of and satisfied by the enormous progress that we've achieved in AI thus far, especially if we think of the incremental improvements that we'll see in information management based on automatic understanding in even just the next 10 years. Bio: Marco Varone, founder, president and CTO of Expert System, is one of the leading experts on semantic technology and natural language processing. He has worked in the field application of semantic technology in every advanced context: search engines, text analytics, natural language interfaces, Q & A systems, automatic categorization and many others.

Last time we wrote an image classifier using TensorFlow for Poets. This time, we'll write a basic one using TF.Learn. To make it easier for you to try this out, I wrote a Jupyter Notebook for this episode -- https://goo.gl/NNlMNu This is a great way to get all the dependencies installed and properly configured. I've linked some additional notebooks below you can try out, too.

As we previously reported, artificial intelligence (AI) is not some Asimovian fantasy, nor an extravagance best left to starch-smocked scientists clinking beakers together in an underground laboratory. It is an opportunity to create tools that save money, save lives and improve life in ways that can't be measured. Stated very simply, AI is the name given to computer systems that attempt to replicate human intelligence and learning. There's great difficulty in finding a satisfying definition for AI because the definition of intelligence itself conjures big questions of consciousness and being that have not yet been resolved by science and philosophy. A practical way to approach AI is to consider why it exists in its current manifestations.

Debt of any kind--if not addressed--is a time bomb waiting to explode. We can easily relate to this with reference to finance. The comparison between technical complexity and debt was first drawn in 1992. In an experience report, Ward Cunningham alerted the industry to the problem and in doing so, coined the term "technical debt." "Shipping first time code is like going into debt. A little debt speeds development so long as it is paid back promptly with a rewrite… The danger occurs when the debt is not repaid. Every minute spent on not-quite-right code counts as interest on that debt. Entire engineering organizations can be brought to a stand-still under the debt load of an unconsolidated implementation, object-oriented or otherwise."--