The human face constantly conveys information, both consciously and subconsciously. However, as basic as it is for humans to visually interpret this information, it is quite a big challenge for machines. Conventional semantic facial feature recognition and analysis techniques are already in use and are based on physiological heuristics, but they suffer from lack of robustness and high computation time. This thesis aims to explore ways for machines to learn to interpret semantic information available in faces in an automated manner without requiring manual design of feature detectors, using the approach of Deep Learning. This thesis provides a study of the effects of various factors and hyper-parameters of deep neural networks in the process of determining an optimal network configuration for the task of semantic facial feature recognition. This thesis explores the effectiveness of the system to recognize the various semantic features (like emotions, age, gender, ethnicity etc.) present in faces. Furthermore, the relation between the effect of high-level concepts on low level features is explored through an analysis of the similarities in low-level descriptors of different semantic features. This thesis also demonstrates a novel idea of using a deep network to generate 3-D Active Appearance Models of faces from real-world 2-D images. For a more detailed report on this work, please see [arXiv:1512.00743v1].

This paper explores the capabilities of convolutional neural networks to deal with a task that is easily manageable for humans: perceiving 3D pose of a human body from varying angles. However, in our approach, we are restricted to using a monocular vision system. For this purpose, we apply a convolutional neural network approach on RGB videos and extend it to three dimensional convolutions. This is done via encoding the time dimension in videos as the 3\ts{rd} dimension in convolutional space, and directly regressing to human body joint positions in 3D coordinate space. This research shows the ability of such a network to achieve state-of-the-art performance on the selected Human3.6M dataset, thus demonstrating the possibility of successfully representing temporal data with an additional dimension in the convolutional operation.

Summary: Here's some background on how 3rd generation Spiking Neural Nets are progressing and news about a first commercial rollout. Recently we wrote about the development of AI and neural nets beyond the second generation Convolutional and Recurrent Neural Nets (CNNs / RNNs) which have come on so strong and dominate the current conversation about deep learning. Our research shows that the next generation of neural nets is most likely to be led by Spiking Neural Nets (SNNs) that are a return to the'strong' AI tradition and closely mimic actual brain function. Unlike CNNs that fire signals to every one of their deep layer connections every time, SNNs are modeled after the fact that in the brain neurons do not constantly communicate with one another. Rather they communicate in spikes of signals or more correctly short trains of spiking signals.

Why is it that every time humans develop a really clever computer system in the movies, it seems intent on killing every last one of us at its first opportunity? In Stanley Kubrick's masterpiece, 2001: A Space Odyssey, HAL 9000 starts off as an attentive, if somewhat creepy, custodian of the astronauts aboard the USS Discovery One, before famously turning homicidal and trying to kill them all. In The Matrix, humanity's invention of AI promptly results in human-machine warfare, leading to humans enslaved as a biological source of energy by the machines. In Daniel H. Wilson's book Robopocalypse, computer scientists finally crack the code on the AI problem, only to have their creation develop a sudden and deep dislike for its creators. Is Siri just a few upgrades away from killing you in your sleep? And you're not an especially sentient being yourself if you haven't heard the story of Skynet (see The Terminator, T2, T3, etc.) The simple answer is that -- movies like Wall-E, Short Circuit, and Chappie, notwithstanding -- Hollywood knows that nothing guarantees box office gold quite like an existential threat to all of humanity. Whether that threat is likely in real life or not is decidedly beside the point. How else can one explain the endless march of zombie flicks, not to mention those pesky, shark-infested tornadoes? The reality of AI is nothing like the movies. Siri, Alexa, Watson, Cortana -- these are our HAL 9000s, and none seems even vaguely murderous. The technology has taken leaps and bounds in the last decade, and seems poised to finally match the vision our artists have depicted in film for decades. Is Siri just a few upgrades away from killing you in your sleep, or is Hollywood running away with a tired idea? Looking back at the last decade of AI research helps to paint a clearer picture of a sometimes frightening, sometimes enlightened future. An increasing number of prominent voices are being raised about the real dangers of humanity's continuing work on so-called artificial intelligence.

Apple employs their new Artificial Intelligence (AI) director Ruslan Salakhutdinov, a leading expert in the field. He is tasked to ensure that Siri and other related products will take advantage of all the relevant breakthroughs released by academic experts from AI research. He is scheduled to discuss his research for the MIT Technology Review conference at EmTech MIT 2016 to be held this week. Salakhutdinov is an associate professor at Carnegie Mellon University in the Machine Learning Department, working in the field of statistical machine learning. His research revolves around deep learning and a series of very large neural networks which allows the computer to learn and carry out complex tasks by absorbing extensive amounts of patterns and training examples.

To accomplish the 5.9 percent error rate, which beats a 6.3 percent record set just last month, the Microsoft team leveraged neural language models resembling associative word clouds. That is, a word like "fast" resides much closer to "fast" than it does to "slow". This allowed the speech recognition engine to generalize between words and better recognize them in context. The team relied on Microsoft's homegrown deep learning Computational Network Toolkit to develop its record-setting algorithm. The team's next goal is to improve the engine's robustness so that it can be used in real-life situations such as on crowded city streets or while driving.

In August, 2016, Intel is bolstering its artificial intelligence efforts by acquiring Nervana Systems for 400 million, a two-year-old startup considered among the leaders in developing machine learning technology. In a video, Nervana's Naveen Rao discussed deep learning, a form of machine learning loosely inspired by the brain. Naveen explores the benefits of deep learning over other machine-learning techniques, recent advances in the field, the deep learning workflow, challenges in developing and deploying deep learning-based solutions, and the need for standardized tools for building and scaling deep learning solutions. Convolutional Neural Nets are the main model. They are good for vision systems.

Microsoft has made a major breakthrough in speech recognition, creating a technology that recognizes the words in a conversation as well as a person does. In a paper published Monday, a team of researchers and engineers in Microsoft Artificial Intelligence and Research reported a speech recognition system that makes the same or fewer errors than professional transcriptionists. The researchers reported a word error rate (WER) of 5.9 percent, down from the 6.3 percent WER the team reported just last month. The 5.9 percent error rate is about equal to that of people who were asked to transcribe the same conversation, and it's the lowest ever recorded against the industry standard Switchboard speech recognition task. "We've reached human parity," said Xuedong Huang, the company's chief speech scientist.

Reportedly announced, Google DeepMind is now capable of teaching itself depending on the information it already possessed. Founded in 2010, Google's British Artificial Intelligence company DeepMind is now on the edge of a revolutionary invention. DeepMind is able to learn, or we can say, to teach itself based on data it already holds. This leads to a completely self-contained learning. According to their DNC's -Differentiable Neural Computer- anwers, it can answer questions about the relationships in a family.

In part 1 we looked at using linear regression, with the aid of a spreadsheet, to see if we could predict within a reasonable tolerance predict what Darcey Bussell's scoring would be based on Craig Revel Horwood's score. No big deal, it worked quite well, it took less thank five minutes and didn't interfere with me making a cup of tea. Linear Regression is all well and good but this is 2016, this is the year where every Northern Ireland company decides it's going to do artificial intelligence and machine learning with hardly any data…. So, we're going to upgrade the Darcey Coefficient and go all Techcrunch/Google/DeepMind on it, Darcey's predictions are now going to be an Artificial Neural Network! They're good but held with a small amount of skepticism.