Last week I gave a presentation around Artificial Intelligence to Product people: PO's, PMO's and others. I recalled how my geek day is using Alexa, Slack bots, x.ai, Smart Lighting & how happy I am of in living in such an era. Many of the smart, cognitive and automation things that we see are simple workflows, and they do not have AI, per se, on it (a discussion for another post). In a great conversation I tried to pass my message along, elucidate some concepts and show that Machine Learning algorithms can be used in lots of products -- so, you can have AI in the backend of your app, software, API, website, … Speech recognition, recommendation systems, image tagging, NLP, search optimisation, clustering and many others features that your product can react to it, providing a personalised experience. Telling the history of AI, the overselling of the 50's and 60's, the AI Winter and the recent boom I try to make everyone cautious about overselling its capabilities.

There have been many different attempts to create a J.A.R.V.I.S type of AI system to act as a personal assistant, able to interact with you and automate things. These have been from high profile people like Mark Zuckerberg (Facebook) creating a version on J.A.R.V.I.S in his home, voiced by Morgan Freeman. This system has linked simple things like lights, music and toasters which all have IoT devices that you can link together, to more elaborate items such as a t-shirt dispenser, motors to open the curtains and face recognition door system. The brains behind are based on a chat bot and mobile app that the user can integrate with. Other J.A.R.V.I.S examples and developments range from simple lights and desktop interactions, to Amazon Alexa being used to control interactions with apps called J.A.R.V.I.S.

In addition to Workplace Hub, Konica Minolta Laboratory Europe is carrying out some of its research and development activities about the Cognitive Hub, an integrated platform that can learn, adapt and enable organisations to make more insightful and impactful decisions in areas such as investments, business models, new products and services. AI Summit Event Director, George Kipouros, is looking forward to the next event in the AI Summit Series. A number of leading organizations spanning finance, law, healthcare, manufacturing, transport, energy, education and many more are looking to implement the technologies or have already started. Beyond Konica Minolta, the conference programmes includes interesting keynotes on AI projects from JP Morgan, The Carlyle Group, Disney, Philips and many more. "AI is being implemented by leading organizations in a broad range of industries and we are very excited to host Konica Minolta alongside key industry names that include IBM Watson, Microsoft, Digitate, Publicis.

Hedge fund giant Man Group hopes to show portfolio managers how to make smarter decisions using artificial intelligence, with its discretionary investment division GLG hiring its first head of machine learning. William Ferreira, who has a PhD in theoretical computer science, has taken up the new role almost three years after leaving Man's AHL arm, which has been researching and using machine learning for years. Machine learning sifts through data to detect patterns, making it useful for hedge funds to predict market moves while social media platforms such as Facebook rely on the technique to customise the posts members see. Mr Ferreira, who most recently worked at hedge fund Florin Court Capital, will teach GLG's traders how to analyse news and social media, market events and announcements via machine learning techniques. His hire comes months after Man Group announced a new research professorship in machine learning at Oxford University, a role that will focus on financial markets as the sector races to exploit data analytics.

We provide a comparative study of several widely used off-policy estimators (Empirical Average, Basic Importance Sampling and Normalized Importance Sampling), detailing the different regimes where they are individually suboptimal. We then exhibit properties optimal estimators should possess. In the case where examples have been gathered using multiple policies, we show that fused estimators dominate basic ones but can still be improved.

We construct $\bf genRBF$ kernel, which generalizes the classical Gaussian RBF kernel to the case of incomplete data. We model the uncertainty contained in missing attributes making use of data distribution and associate every point with a conditional probability density function. This allows to embed incomplete data into the function space and to define a kernel between two missing data points based on scalar product in $L_2$. Experiments show that introduced kernel applied to SVM classifier gives better results than other state-of-the-art methods, especially in the case when large number of features is missing. Moreover, it is easy to implement and can be used together with any kernel approaches with no additional modifications.

We describe the SemEval task of extracting keyphrases and relations between them from scientific documents, which is crucial for understanding which publications describe which processes, tasks and materials. Although this was a new task, we had a total of 26 submissions across 3 evaluation scenarios. We expect the task and the findings reported in this paper to be relevant for researchers working on understanding scientific content, as well as the broader knowledge base population and information extraction communities.

Most of us take for granted how well our brains can relay instructions to our limbs. Doctors and engineers have been trying for years to grant that same surety to those with prosthetic limbs. But interfacing the biological and technological is tricky. There have been some impressive advances in this area of research, but they usually require the prosthetic to be directly wired into the patient's brain--not exactly practical. Doctors in the Netherlands are now testing a new type of prosthetic "click-on" arm that is connected to the patient's existing nerves.

Here at The Human OS, we are slightly obsessed with matchups between artificial intelligence and doctors. In many experiments (though not yet in many clinics), AI systems are showing great promise in diagnosing diseases, analyzing medical images, and predicting health outcomes. They've even performed better than human doctors in certain tasks like surgical stitching and diagnosing autism in infants. Now, in the latest win for AI medicine, researchers at the University of Nottingham in the UK created a system that scanned patients' routine medical data and predicted which of them would have heart attacks or strokes within 10 years. When compared to the standard method of prediction, the AI system correctly predicted the fates of 355 more patients.

Use our LinkedIn Login to download this post to PDF or save it to MyLibrary! Everyone has been hearing about AI, some have been hearing about NLP - and everyone has an opinion, belief, or thought about AI. However, that opinion, belief, or thought about AI (and/or NLP) is fully dependent upon the voices of whom everyone has chosen to listen. We are at a stage in our rate of change of technology where we have to let go of how we learned things in the past -- and step into a new stage of keeping ourselves always available to learn, no matter what we believe and/or think. We cannot, more than ever before, solve our problems with the same thinking we used to create them (said Albert Einstein nearly 100 years ago!).