With the increasing popularity of video sharing websites such as YouTube and Facebook, multimodal sentiment analysis has received increasing attention from the scientific community. Contrary to previous works in multimodal sentiment analysis which focus on holistic information in speech segments such as bag of words representations and average facial expression intensity, we develop a novel deep architecture for multimodal sentiment analysis that performs modality fusion at the word level. In this paper, we propose the Gated Multimodal Embedding LSTM with Temporal Attention (GME-LSTM(A)) model that is composed of 2 modules. The Gated Multimodal Embedding alleviates the difficulties of fusion when there are noisy modalities. The LSTM with Temporal Attention performs word level fusion at a finer fusion resolution between input modalities and attends to the most important time steps. As a result, the GME-LSTM(A) is able to better model the multimodal structure of speech through time and perform better sentiment comprehension. We demonstrate the effectiveness of this approach on the publicly-available Multimodal Corpus of Sentiment Intensity and Subjectivity Analysis (CMU-MOSI) dataset by achieving state-of-the-art sentiment classification and regression results. Qualitative analysis on our model emphasizes the importance of the Temporal Attention Layer in sentiment prediction because the additional acoustic and visual modalities are noisy. We also demonstrate the effectiveness of the Gated Multimodal Embedding in selectively filtering these noisy modalities out. Our results and analysis open new areas in the study of sentiment analysis in human communication and provide new models for multimodal fusion.

The main inspiration for this blog post is based on the work I did on Bayesian Neural Networks with my friend Brian Trippe at the Computational and Biological Learning Lab in Cambridge University. I highly recommend anyone to read Brian's thesis on variational inference in neural networks. Disclaimer: At the Computational and Biological Learning Lab Bayesian machine learning techniques are unapologetically taught as the way forward. As such, be aware of potential bias in this blog post. For example in image classification, x represents an image and y the corresponding image label.

Artificial intelligence and machine learning are all the rage--and for good reason. But researchers claim the brain doesn't actually use the regions identified by machine learning to perform a task. Rather, these algorithms reflect the mental associations related to the task. One author of the study, published online Jan. 31 in the Proceedings on the National Academy of Sciences, Anne-Lise Giraud with the department of fundamental neuroscience at the University of Geneva in Switzerland, said in a release that "learning algorithms are intelligent but ignorant. They are very sensitive and use all the information in the signals. However, they do not allow us to know whether this information was used to perform the task, or if it reflects the consequences of this task--in other words, spreading information in our brain."

Artificial intelligence (AI) is an increasingly important part of the financial technology sector, specifically in analysing stocks and providing insights into the markets that human analysts alone can't make. As such, AI-driven funds hold an increasingly large portion of the market, moving this technology into the mainstream. The result is a large opportunity for investors using this technology or investing in the companies producing it. Significant steps are being taken by companies such as AnalytixInsight (ATIXF Profile), whose flagship CapitalCube cloud-based analytics empowers investors to evaluate the potential of companies and portfolios. Other companies are also taking note of AI's increasing value.

At Search Party we are in the business of creating intelligent recruitment software. One of the problems we deal with is matching candidates and vacancies in order to create a recommendation engine. This usually requires parsing, interpreting and normalising messy, semi-/unstructured, textual data from résumés and vacancies, which is where the following come in: conditional random fields, bag-of-words, TF-IDFs, WordNet, statistical analysis, but also a lot of manual work done by linguists and domain experts for the creation of synonym lists, skill taxonomies, job title hierarchies, knowledge bases or ontologies. While these concepts are valuable for the problem we try to solve, they also require a certain amount of manual feature engineering and human expertise. This expertise is certainly a factor that makes these techniques valuable, but the question remains whether more automated approaches can be used to extract knowledge about the job space to complement these more traditional approaches.

Dr. Claudio Lima is an industry thought leader and entrepreneur in Advanced Digital Transformation. He is leading advanced research and has created the industry vision and project initiatives to develop smart contract-driven autonomous bot machines (auto-bots), using AI and edge-IOT computing. He's passionate for Blockchain Distributed Ledger and Crypto-Current technologies. Previously, Dr. Lima served as Global Smart Grid CTO of Huawei Technologies in Europe-Asia-Pacific and as Distinguished Member of Technical Staff (DMTS)/Sr. He has set up and led global advanced research and engineering projects with collaboration in a wide variety of industries.

Technology company Continental is opening of a Deep Machine Learning Competence Centre in Budapest in May 2018 and with that adding 100 new jobs in the region. "Artificial intelligence is a core competency in the development of automated driving. We are expanding our expertise in the area of Deep Machine Learning to enable automated driving and to support our Vision Zero – a future without accidents," Karl Haupt, head of Continental's Advanced Driver Assistance Systems business unit, said in a press release. The Budapest Competence Centre for Deep Machine Learning will be integrated in an existing Global Software Factory network with other development locations inside the Advanced Driver Assistance Systems business unit. "More and more technological and development processes are added to the well-prosperous automotive operation at Continental's domestic units. Our high-tech developments create a demand for highly qualified labour, which can be one of the guarantees for the future success and sustainability" – added Daniel Rabai, Head of Focus County at Continental in Hungary.

Artificial intelligence has increasingly been integrated into the weapons systems of the world's leading militaries, and at least one expert has said the futuristic technology may soon be the subject of a new Cold War. In a piece published Tuesday by The Conversation, North Dakota State University assistant professor Jeremy Straub argued that unlike the nuclear weapons that dominated much of the 21st century arms race between the U.S. and the Soviet Union, the use of cyberweapons and artificial intelligence largely remained "fair game," even as tensions again flared between the rivals. Both countries have invested heavily in developing new tools to wage war on this new front, but Russia particularly has sought to use it as an opportunity to upstage the more conventionally powerful U.S. Related: U.S. is losing to Russia and China in war for artificial intelligence, report says "Now, more than 30 years after the end of the Cold War, the U.S. and Russia have decommissioned tens of thousands of nuclear weapons. Any modern-day cold war would include cyberattacks and nuclear powers' involvement in allies' conflicts," wrote Straub, who was also associate director of the university's Institute for Cyber Security Education and Research, in his article. "It's already happening," he added.

Google's parent company Alphabet has reported lower than expected profits in the last three months of 2017 after higher costs offset an increase in advertising sales. Revenue for the three months ending December 31 was $32.3 billion (£22.6bn), a rise of 24% on the same period in 2016. Overall, Alphabet reported a loss of $3 billion (£2.1bn) for the fourth quarter as it set aside $11 billion for taxes - an estimated $9.9 billion (£6.9bn) was for taxes on repatriated earnings. Excluding the tax provision, Alphabet would have posted a profit of $6.8 billion (£4.7bn), falling short of the $7 billion analysts had predicted. The tech giant's shares slid 2.3 per cent in after-hours trade on the results, highlighting concerns about the rising costs of projects such as the Waymo's self-driving car service, and the fact that profits were weaker than expected. The company also used its earning report to confirm that current board member John Hennessy has been named Alphabet chairman following the departure of Eric Schmidt in December.

The pharmaceutical industry is on the cusp of an artificially intelligent wave that promises enormous opportunities, but also immense challenges. The onset of artificial intelligence (AI) in the modern world has led to the prophesying of a seismic shift whereby many of the jobs we are familiar with today will disappear in favour of algorithms and robots. In the area of pharma, AI promises not only to overhaul how the industry produces and designs medication, but could also lead to the discovery of whole new medication in the space of a few hours – discoveries that would take a human years. To take just one recent example, scientists at the University of Cambridge announced that, with the help of an AI-powered robot called Eve, they were able to identify a possible antimalarial drug found in a common toothpaste ingredient. While this is just an example of what relatively small-scale research can do, think about the potential for AI in the vast, mega-pharma corporations with much greater resources at their disposal. One of those keen to understand how AI can make a real impact in pharma is Dr Muhammed Ali, MSD International's executive director for healthcare solutions strategy across Europe and Canada in its commercial and operations team.