Hurry--best price ends February 2. What will 2018 bring in AI? Here's what's on our radar. As in recent years, new deep learning architectures and (distributed) training algorithms will lead to impressive results and applications in a range of domains, including computer vision, speech, and text. Expect to see companies make progress on efficient algorithms for training, inference, and data processing on edge devices. At the same time, collaboration between machine learning experts will produce interesting breakthroughs--examples include work that draws from Bayesian methods and deep learning and work on neuroevolution and gradient-based deep learning. However, as successful as deep learning has been, our level of understanding of why it works so well is still lacking.

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In an effort to understand the meaning of the intermediate representations captured by deep networks, recent papers have tried to associate specific semantic concepts to individual neural network filter responses, where interesting correlations are often found, largely by focusing on extremal filter responses. In this paper, we show that this approach can favor easy-to-interpret cases that are not necessarily representative of the average behavior of a representation. A more realistic but harder-to-study hypothesis is that semantic representations are distributed, and thus filters must be studied in conjunction. In order to investigate this idea while enabling systematic visualization and quantification of multiple filter responses, we introduce the Net2Vec framework, in which semantic concepts are mapped to vectorial embeddings based on corresponding filter responses. By studying such embeddings, we are able to show that 1., in most cases, multiple filters are required to code for a concept, that 2., often filters are not concept specific and help encode multiple concepts, and that 3., compared to single filter activations, filter embeddings are able to better characterize the meaning of a representation and its relationship to other concepts.

Machine learning solutions, in particular those based on deep learning methods, form an underpinning of the current revolution in "artificial intelligence" that has dominated popular press headlines and is having a significant influence on the wider tech agenda. In this talk I will give an overview of where we are now with machine learning solutions, and what challenges we face both in the near and far future. These include practical application of existing algorithms in the face of the need to explain decision making, mechanisms for improving the quality and availability of data, dealing with large unstructured datasets.

Researchers at Uber and Google are working on modifications to the two most popular deep-learning frameworks that will enable them to handle probability. This will provide a way for the smartest AI programs to measure their confidence in a prediction or a decision--essentially, to know when they should doubt themselves. Deep learning, which involves feeding example data to a large and powerful neural network, has been an enormous success over the past few years, enabling machines to recognize objects in images or transcribe speech almost perfectly. But it requires lots of training data and computing power, and it can be surprisingly brittle. Somewhat counterintuitively, this self-doubt offers one fix.

This year I attended and presented a poster at the Medical Imaging Meets NIPs workshop. The workshop focused on bringing together professionals from both the medical imaging and machine learning communities. Altogether there were eleven talks and two poster sessions. Presentations and posters generally discussed segmentation, classification, and/or image reconstruction. Before coming to this workshop I must admit that I did not fully understand the value of image segmentation.

Here at The Next Platform, we tend to keep a close eye on how the major hyperscalers evolve their infrastructure to support massive scale and evermore complex workloads. Not so long ago the core services were relatively standard transactions and operations, but with the addition of training and inferencing against complex deep learning models--something that requires a two-handed approach to hardware--the hyperscale hardware stack has had to quicken its step to keep pace with the new performance and efficiency demands of machine learning at scale. While not innovating on the custom hardware side quite the same way as Google, Facebook has shared some notable progress in fine-tuning its own datacenters. From its unique split network backbone, neural network-based viz system, to large-scale upgrades to its server farms and its work honing GPU use, there is plenty to focus on infrastructure-wise. For us, one of the more prescient developments from Facebook is its own server designs which now serve over 2 billion accounts as of the end of 2017, specifically its latest GPU-packed Open Compute based approach.

Data is the unreasonably effective force behind the current deep learning breakthroughs. Without a sufficient amount of data, even the most intricate neural network powered by the best hardware would fall short of human-level performance. As video data is becoming ubiquitous, we will rely on machines to reason and extract information from numerous videos made available by social media and visual-enabled devices. Supervised learning will drive the most commercial successes in deep learning but its data collection process is flawed. Finding no suitable video dataset for teaching machines to understand the world, we developed crowd-acting, an industrial data collection approach inspired by previous contributions, particularly Hollywood in Homes and its dataset Charades (Sigurdsson et al.).

Prior to rolling out DeepThinQ 1.0 to all of its business divisions, LG Electronics grabbed the chance to explain in full detail what this deep-learning based artificial intelligence technology is all about. On Tuesday, LG took to its online newsroom to talk about its new AI platform, which consumers will soon have access to via the upcoming mobile devices and appliances that the company is launching starting this year. According to the company, it developed DeepThinQ 1.0 last year following the establishment of its South Korea-based Artificial Intelligence Lab. LG's primary goal in making DeepThinQ 1.0 is to have a platform that would allow easy and smooth integration of AI into its wide range of products. This way, its developers wouldn't have a hard time including deep-learning technologies in future releases.

The seeds were sown back in the 1950s when the first artificial neural network was created. Since then, progress has been rapid, with the structure of the neuron being "re-invented" artificially. Computers and mobiles have now become powerful enough to identify objects from images. Not just images, they can chat with you as well! That's not all--they can drive, make supersonic calculations, and help businesses solve the most complicated problems (more users, revenue, etc). But, what is driving all these inventions? With increasing open source contributions, R language now provides a fantastic interface for building predictive models based on neural networks and deep learning.