IBM (NYSE: IBM) today announced the first recipients and list of global women leaders and pioneers in AI for business. The list recognizes and celebrates women across a variety of industries and geographies for pioneering the use of AI to advance their companies in areas such as innovation, growth, and transformation. IBM will celebrate the honorees during an inaugural recognition event on June 12, 2019 at the IBM Watson Experience Center in New York, New York where the women will share their experiences leading AI initiatives in their organizations. Students from IBM's P-Tech program will attend to hear from these leaders who have applied AI technology in diverse and meaningful ways to help drive business innovation. "Artificial Intelligence is poised to drive dramatic advances in every industry," said Michelle Peluso, SVP, Digital Sales & CMO, IBM, who also serves as Leader of IBM's Women's Initiative.

A breakthrough discovery shows that pterodactyls could fly from birth, something no other species before or since has been able to do. And British scientists said that the revelation has a'profound impact' on our understanding of the reptiles. The common belief was the pterodactyls, like birds and bats, only took to the air once they were fully grown. A new study shows pterodactyls could fly from birth, something no other species before or since can do. The findings have a'profound impact' on our understanding of reptiles Pterodactyls used both their arms and legs to push themselves off the ground during take-off, in a manoeuvre known as the'quadrupedal launch'. They were almost as tall as a giraffe with wing spans of around 32ft (10 metres).

Knowledge bases (KBs) are the backbone of many ubiquitous applications and are thus required to exhibit high precision. However, for KBs that store subjective attributes of entities, e.g., whether a movie is "kid friendly", simply estimating precision is complicated by the inherent ambiguity in measuring subjective phenomena. In this work, we develop a method for constructing KBs with tunable precision--i.e., KBs that can be made to operate at a specific false positive rate, despite storing both difficult-to-evaluate subjective attributes and more traditional factual attributes. The key to our approach is probabilistically modeling user consensus with respect to each entity-attribute pair, rather than modeling each pair as either True or False. Uncertainty in the model is explicitly represented and used to control the KB's precision. We propose three neural networks for fitting the consensus model and evaluate each one on data from Google Maps--a large KB of locations and their subjective and factual attributes. The results demonstrate that our learned models are well-calibrated and thus can successfully be used to control the KB's precision. Moreover, when constrained to maintain 95% precision, the best consensus model matches the F-score of a baseline that models each entity-attribute pair as a binary variable and does not support tunable precision. When unconstrained, our model dominates the same baseline by 12% F-score. Finally, we perform an empirical analysis of attribute-attribute correlations and show that leveraging them effectively contributes to reduced uncertainty and better performance in attribute prediction.

This paper presents the first comprehensive empirical study demonstrating the efficacy of the Brain Floating Point (BFLOAT16) half-precision format for Deep Learning training across image classification, speech recognition, language modeling, generative networks and industrial recommendation systems. BFLOAT16 is attractive for Deep Learning training for two reasons: the range of values it can represent is the same as that of IEEE 754 floating-point format (FP32) and conversion to/from FP32 is simple. Maintaining the same range as FP32 is important to ensure that no hyper-parameter tuning is required for convergence; e.g., IEEE 754 compliant half-precision floating point (FP16) requires hyper-parameter tuning. In this paper, we discuss the flow of tensors and various key operations in mixed precision training, and delve into details of operations, such as the rounding modes for converting FP32 tensors to BFLOAT16. We have implemented a method to emulate BFLOAT16 operations in Tensorflow, Caffe2, IntelCaffe, and Neon for our experiments. Our results show that deep learning training using BFLOAT16 tensors achieves the same state-of-the-art (SOTA) results across domains as FP32 tensors in the same number of iterations and with no changes to hyper-parameters.

In the following, I share the key learnings of the webinar. AI is not a secret sauce and requires lots of good data to create real value. Companies need to first separate the hype from the actual capabilities of AI, defining what AI means for them and how it might create value. Moving an entire company towards the adoption of AI is a challenging task and needs lots of educational effort. AI is not the solution to all problems. Building products do not start with thinking about AI but finding a meaningful problem that once solved adds value for the customer or user.

Humans aren't the only species whose mental state is affected when they lose their lover. Female cichlids, a type of monogamous fish that primarily dwells in South America, become depressed and lovesick when their mate is removed and they're placed with a non-preferred male partner, a new study has found. Researchers came to this conclusion after the female fish took longer to investigate boxes that either contained food or were empty, demonstrating symptoms of apathy. Female cichlids, a type of fish that primarily dwells in South America, become depressed and lovesick when their mate is removed and they're placed with a non-preferred male partner In what's believed to be a first-of-its-kind study, researchers say they've determined fish can form attachments to sexual partners. Through a series of cognitive tests, they found that female fish were more likely to take on a'glass half-full' mental state when they remained with their chosen partners.

We explore using latent natural language instructions as an expressive and compositional representation of complex actions for hierarchical decision making. Rather than directly selecting micro-actions, our agent first generates a latent plan in natural language, which is then executed by a separate model. We introduce a challenging real-time strategy game environment in which the actions of a large number of units must be coordinated across long time scales. We gather a dataset of 76 thousand pairs of instructions and executions from human play, and train instructor and executor models. Experiments show that models using natural language as a latent variable significantly outperform models that directly imitate human actions. The compositional structure of language proves crucial to its effectiveness for action representation. We also release our code, models and data.

This thesis presents new methods for unsupervised learning of distributed representations of words and entities from text and knowledge bases. The first algorithm presented in the thesis is a multi-view algorithm for learning representations of words called Multiview Latent Semantic Analysis (MVLSA). By incorporating up to 46 different types of co-occurrence statistics for the same vocabulary of english words, I show that MVLSA outperforms other state-of-the-art word embedding models. Next, I focus on learning entity representations for search and recommendation and present the second method of this thesis, Neural Variational Set Expansion (NVSE). NVSE is also an unsupervised learning method, but it is based on the Variational Autoencoder framework. Evaluations with human annotators show that NVSE can facilitate better search and recommendation of information gathered from noisy, automatic annotation of unstructured natural language corpora. Finally, I move from unstructured data and focus on structured knowledge graphs. I present novel approaches for learning embeddings of vertices and edges in a knowledge graph that obey logical constraints.

In-game win probability is a statistical metric that provides a sports team's likelihood of winning at any given point in a game, based on the performance of historical teams in the same situation. In-game win-probability models have been extensively studied in baseball, basketball and American football. These models serve as a tool to enhance the fan experience, evaluate in game-decision making and measure the risk-reward balance for coaching decisions. In contrast, they have received less attention in association football, because its low-scoring nature makes it far more challenging to analyze. In this paper, we build an in-game win probability model for football. Specifically, we first show that porting existing approaches, both in terms of the predictive models employed and the features considered, does not yield good in-game win-probability estimates for football. Second, we introduce our own Bayesian statistical model that utilizes a set of eight variables to predict the running win, tie and loss probabilities for the home team. We train our model using event data from the last four seasons of the major European football competitions. Our results indicate that our model provides well-calibrated probabilities. Finally, we elaborate on two use cases for our win probability metric: enhancing the fan experience and evaluating performance in crucial situations.

Noelia Jiménez Martínez is Head of Data Science and Astrophysics at Unbound. She holds a PhD in Numerical Astrophysics from the UNLP (La Plata, Argentina) applied to Galaxy Formation and Chemical Evolution. Before transitioning from Academia, she was an Astrophysics Researcher at the University of St Andrews (Scotland), and previously had several postdocs positions in different universities across Europe that gave her the chance to collaborate with a huge diversity of people from several fields and backgrounds. Prior to joining Unbound she worked as a Data Science Consultant (Pivigo) in London, where she managed several data science teams working with big-to-small companies (Barclays, Criteo, Royal Mail, startups, etc) and lectured academics transitioning to Industry in the S2DS (science to data science school) in fields as Machine Learning, Statistics and Deep Learning. She is also the author of a book exploring/building empathy and social skills among academics from'hard' sciences: 'Data: A Guide to Humans'.