AI's rapid evolution is producing an explosion in new types of hardware accelerators for machine learning and deep learning. Some people refer to this as a "Cambrian explosion," which is an apt metaphor for the current period of fervent innovation. It refers to the period about 500 million years ago when essentially every biological "body plan" among multicellular animals appeared for the first time. From that point onward, these creatures--ourselves included--fanned out to occupy, exploit, and thoroughly transform every ecological niche on the planet. The range of innovative AI hardware-accelerator architectures continues to expand.

Early last year, a Microsoft research project dubbed DeepCoder announced that it had made progress creating AI that could write its own programs. Such a feat has long captured the imagination of technology optimists and pessimists alike, who might consider software that creates its own software as the next paradigm in technology -- or perhaps the direct route to building the evil Skynet. As with most machine learning or deep learning approaches that make up the bulk of today's AI, DeepCoder was creating code that it based on large numbers of examples of existing code that researchers used to train the system. The result: software that ended up assembling bits of human-created programs, a feat Wired Magazine referred to as "looting other software." And yet, in spite of DeepCoder's PR faux pas, the idea of software smart enough to create its own applications remains an area of active research, as well as an exciting prospect for the digital world at large.

One of the biggest challenges with deep learning is explaining to customers and regulators how the models get their answers. In many cases, we simply don't know how the models generated their answers, even if we're very confident in the answers themselves. However, in the age of GDPR, this black box-style of predictive computing will not suffice, which is driving a push by FICO and others to develop explainable AI. Describing deep learning as a black box is not meant to denigrate the practice. After, all, we're thrilled that, when we build a convolutional neural network with hundreds of input variables and more than a thousand hidden layers (as the biggest CNNs are), it just works.

It wasn't that long ago that Deepmind's AlphaGo proved it could play the game better than the best humans. From the standpoint of the range of possible future moves, the game of Go is not a searchable problem. It represents search spaces that are astronomically larger than all the potential moves in chess. Yet, the individual moves are far simpler and more atomic than chess (and almost any other game) partly because of the incredible simplicity of the rules combined with a giant catalog of hundreds of thousands of human played games. Because it was relatively easy to have it play a large number (countless millions) of games against itself, the game is a good fit for deep learning.

Hundreds of organisations and thousands of techies, including Elon Musk, Demis Hassabis from Google's DeepMind, and the head of the Chocolate Factory's AI lab Jeff Dean have promised never to support the development of autonomous weapons. The pledge was organised by the Future of Life Institute, an outreach geroup focused on tackling existential risks. It was co-founded by a group of researchers, including Max Tegmark, a physics professor at the Massachusetts Institute of Technology, Viktoriya Krakovna, a scientist at DeepMind, and Jann Tallinn, co-founder of Skype. "We will neither participate in nor support the development, manufacture, trade, or use of lethal autonomous weapons," it reads. The promise is based on a "moral component" that machines should be forbidden from making "life-taking decisions."

Machine learning has left its footprint in the technology sector, having created innovative tools and applications that have improved the interactions between users and products. Now, in an ever competitive field, the quantitative finance space is turning its eyes to machine intelligence to stay a step ahead of the curve. Quantitative hedge funds and asset managers have begun building research teams spearheaded by some of the most talented and well known researchers in machine learning and artificial intelligence to create state of the art data analytic tools and trading AIs. Our Clients are looking to experienced machine learning scientists to develop an AI models. The team will focus on using machine intelligence, natural language processing, and deep learning to create the necessary components for a AI dependent trading.

Ultrasound (US) is the most widely used fetal imaging technique. However, US images have limited capture range, and suffer from view dependent artefacts such as acoustic shadows. Compounding of overlapping 3D US acquisitions into a high-resolution volume can extend the field of view and remove image artefacts, which is useful for retrospective analysis including population based studies. However, such volume reconstructions require information about relative transformations between probe positions from which the individual volumes were acquired. In prenatal US scans, the fetus can move independently from the mother, making external trackers such as electromagnetic or optical tracking unable to track the motion between probe position and the moving fetus. We provide a novel methodology for image-based tracking and volume reconstruction by combining recent advances in deep learning and simultaneous localisation and mapping (SLAM). Tracking semantics are established through the use of a Residual 3D U-Net and the output is fed to the SLAM algorithm. As a proof of concept, experiments are conducted on US volumes taken from a whole body fetal phantom, and from the heads of real fetuses. For the fetal head segmentation, we also introduce a novel weak annotation approach to minimise the required manual effort for ground truth annotation. We evaluate our method qualitatively, and quantitatively with respect to tissue discrimination accuracy and tracking robustness.

Systematic compositionality is the ability to recombine meaningful units with regular and predictable outcomes, and it's seen as key to humans' capacity for generalization in language. Recent work has studied systematic compositionality in modern seq2seq models using generalization to novel navigation instructions in a grounded environment as a probing tool, requiring models to quickly bootstrap the meaning of new words. We extend this framework here to settings where the model needs only to recombine well-trained functional words (such as "around" and "right") in novel contexts. Our findings confirm and strengthen the earlier ones: seq2seq models can be impressively good at generalizing to novel combinations of previously-seen input, but only when they receive extensive training on the specific pattern to be generalized (e.g., generalizing from many examples of "X around right" to "jump around right"), while failing when generalization requires novel application of compositional rules (e.g., inferring the meaning of "around right" from those of "right" and "around").

Fuzzing is a commonly used technique designed to test software by automatically crafting program inputs. Currently, the most successful fuzzing algorithms emphasize simple, low-overhead strategies with the ability to efficiently monitor program state during execution. Through compile-time instrumentation, these approaches have access to numerous aspects of program state including coverage, data flow, and heterogeneous fault detection and classification. However, existing approaches utilize blind random mutation strategies when generating test inputs. We present a different approach that uses this state information to optimize mutation operators using reinforcement learning (RL). By integrating OpenAI Gym with libFuzzer we are able to simultaneously leverage advancements in reinforcement learning as well as fuzzing to achieve deeper coverage across several varied benchmarks. Our technique connects the rich, efficient program monitors provided by LLVM Santizers with a deep neural net to learn mutation selection strategies directly from the input data. The cross-language, asynchronous architecture we developed enables us to apply any OpenAI Gym compatible deep reinforcement learning algorithm to any fuzzing problem with minimal slowdown.

The next time you sit down to watch a movie, the algorithm behind your streaming service might recommend a blockbuster that was written by AI, performed by robots, and animated and rendered by a deep learning algorithm. An AI algorithm may have even read the script and suggested the studio buy the rights. It's easy to think that technology like algorithms and robots will make the film industry go the way of the factory worker and the customer service rep, and argue that artistic filmmaking is in its death throes. For the film industry, the same narrative doesn't apply -- artificial intelligence seems to have enhanced Hollywood's creativity, not squelched it. It's true that some jobs and tasks are being rendered obsolete now that computers can do them better.