DeepTech companies combine cutting edge science and advanced engineering with the objective of making a profound impact on humanity. One of the sectors most impacted by DeepTech is AI for Drug Discovery. In the second article in our DeepTech Series, we will focus on AI for Drug Discovery and profile four top tier companies in this sector. Drugs are among the most effective ways to combat disease. Drugs are used to fight cancer, relieve pain, prevent cardiovascular disease, alleviate mental illness, cure infectious disease, and much more.

Disclaimer: The contents of this post, including the images, are from the lectures of "A First Course in Deep Learning" by One Fourth Labs. I am solely responsible for any mistakes that might have crept in while reproducing it. As the name suggests, the purpose of this post is to burst the jargon bubble -- "Deep Learning", along with some of the related AI bubbles floating around in the industry as well as the academia. Probably when you heard about Deep Learning, you might have come across similar jargons like Data Science, AI, Machine Learning, Pattern Recognition etc. Have you wondered: Are they all the same? Why are they known by different names?

Artificial intelligence is the future. Artificial intelligence is science fiction. Artificial intelligence is already part of our everyday lives. All those statements are true, it just depends on what flavor of AI you are referring to. For example, when Google DeepMind's AlphaGo program defeated South Korean Master Lee Se-dol in the board game Go earlier this year, the terms AI, machine learning, and deep learning were used in the media to describe how DeepMind won.

Which framework should you use? A quick search will reveal the intensity of this clash of frameworks. Here is one great article by Kirill Dubovikov. And here is your chance to defend the framework you believe in the most. At its core, the duel is fuelled by the similarity of the two frameworks. Taking all of this into account we can say that almost anything created in one of the frameworks can be replicated in the other at a similar cost.

We began our Turing Lecture June 4, 201811 with a review of computer architecture since the 1960s. In addition to that review, here, we highlight current challenges and identify future opportunities, projecting another golden age for the field of computer architecture in the next decade, much like the 1980s when we did the research that led to our award, delivering gains in cost, energy, and security, as well as performance. "Those who cannot remember the past are condemned to repeat it."--George Software talks to hardware through a vocabulary called an instruction set architecture (ISA). By the early 1960s, IBM had four incompatible lines of computers, each with its own ISA, software stack, I/O system, and market niche--targeting small business, large business, scientific, and real time, respectively. IBM engineers, including ACM A.M. Turing Award laureate Fred Brooks, Jr., thought they could create a single ISA that would efficiently unify all four of these ISA bases. They needed a technical solution for how computers as inexpensive as those with 8-bit data paths and as fast as those with 64-bit data paths could share a single ISA. The data paths are the "brawn" of the processor in that they perform the arithmetic but are relatively easy to "widen" or "narrow." The greatest challenge for computer designers then and now is the "brains" of the processor--the control hardware. Inspired by software programming, computing pioneer and Turing laureate Maurice Wilkes proposed how to simplify control. Control was specified as a two-dimensional array he called a "control store." Each column of the array corresponded to one control line, each row was a microinstruction, and writing microinstructions was called microprogramming.39 A control store contains an ISA interpreter written using microinstructions, so execution of a conventional instruction takes several microinstructions. The control store was implemented through memory, which was much less costly than logic gates. The table here lists four models of the new System/360 ISA IBM announced April 7, 1964. The data paths vary by a factor of 8, memory capacity by a factor of 16, clock rate by nearly 4, performance by 50, and cost by nearly 6.

Scarcely a day goes by without another headline about neural networks: some new task that deep learning algorithms can excel at, approaching or even surpassing human competence. As the application of this approach to computer vision has continued to improve, with algorithms capable of specialized recognition tasks like those found in medicine, the software is getting closer to widespread commercial use--for example, in self-driving cars. Our ability to recognize patterns is a huge part of human intelligence: if this can be done faster by machines, the consequences will be profound. Yet, as ever with algorithms, there are deep concerns about their reliability, especially when we don't know precisely how they work. State-of-the-art neural networks will confidently--and incorrectly--classify images that look like television static or abstract art as real-world objects like school-buses or armadillos. Specific algorithms could be targeted by "adversarial examples," where adding an imperceptible amount of noise to an image can cause an algorithm to completely mistake one object for another.

This is the second of three blogs on the state of the AI chip market and what's to come in 2019. The year will be a festival of new chips and benchmark battles, led by the large companies I mentioned in the first blog ( Intel, Google, AMD, Xilinx, Apple, Qualcomm), and joined by dozens of Silicon Valley startups and Chinese Unicorns sporting valuations in excess of a billion US dollars. In this section, I will cover the most prominent, or at least the loudest, of the startups in the West and in China, where the government is intent on creating an indigenous AI chip industry. We will start with Wave, which appears to be the first to market with silicon for training. Wave Computing had an eventful 2018, taping out its first DataFlow Processing Unit, acquiring MIPS, creating MIPS Open, and shipping its first early systems to a few lucky customers.

Advances in robotics, artificial intelligence, and machine learning are ushering in a new age of automation, as machines match or outperform human performance. Machine intelligence can enable businesses to improve performance by reducing errors, improving sensitivity, quality and speed, and in some cases achieving outcomes that go beyond current resource capabilities. Relevant applications include new product architecture design, rapid material characterization, and life-cycle management tied with a digital strategy that will enable efficient development of products from cradle to grave. In addition, there are also challenges to overcome that must be addressed through a major, sustained research effort that is based solidly on both inferential and computational principles applied to design tailoring of functionally optimized structures. Current applications of structural materials in the aerospace industry demand the highest quality control of material microstructure, especially for advanced rotational turbomachinery in aircraft engines in order to have the best tailored material property. In this paper, deep convolutional neural networks were developed to accurately predict processing-structure-property relations from materials microstructures images, surpassing current best practices and modeling efforts. The models automatically learn critical features, without the need for manual specification and/or subjective and expensive image analysis. Further, in combination with generative deep learning models, a framework is proposed to enable rapid material design space exploration and property identification and optimization. The implementation must take account of real-time decision cycles and the trade-offs between speed and accuracy.

Citizen engagement and technology usage are two emerging trends driven by smart city initiatives. Governments around the world are adopting technology for faster resolution of civic issues. Typically, citizens report issues, such as broken roads, garbage dumps, etc. through web portals and mobile apps, in order for the government authorities to take appropriate actions. Several mediums -- text, image, audio, video -- are used to report these issues. Through a user study with 13 citizens and 3 authorities, we found that image is the most preferred medium to report civic issues. However, analyzing civic issue related images is challenging for the authorities as it requires manual effort. Moreover, previous works have been limited to identifying a specific set of issues from images. In this work, given an image, we propose to generate a Civic Issue Graph consisting of a set of objects and the semantic relations between them, which are representative of the underlying civic issue. We also release two multi-modal (text and images) datasets, that can help in further analysis of civic issues from images. We present a novel approach for adversarial training of existing scene graph models that enables the use of scene graphs for new applications in the absence of any labelled training data. We conduct several experiments to analyze the efficacy of our approach, and using human evaluation, we establish the appropriateness of our model at representing different civic issues.

For dynamic manipulation of flexible objects, we propose an acquisition method of a flexible object motion equation model using a deep neural network and a control method to realize a target state by calculating an optimized time-series joint torque command. By using the proposed method, any physics model of a target object is not needed, and the object can be controlled as intended. We applied this method to manipulations of a rigid object, a flexible object with and without environmental contact, and a cloth, and verified its effectiveness.