18 exponential changes we can expect in the year ahead


Azeem Azhar is a strategist, product entrepreneur, and analyst living in London. He is the curator of the weekly newsletter Exponential View, from which the following is adapted. This is the first year I am presenting predictions for the coming year. I've received some incredibly helpful comments from readers via Twitter. This has encouraged me to stick my head above the parapet.

Quantum Machine Learning: An Overview


At a recent conference in 2017, Microsoft CEO Satya Nadella used the analogy of a corn maze to explain the difference in approach between a classical computer and a quantum computer. In trying to find a path through the maze, a classical computer would start down a path, hit an obstruction, backtrack; start again, hit another obstruction, backtrack again until it ran out of options. Although an answer can be found, this approach could be a very time-consuming. They take every path in the corn maze simultaneously." Thus, leading to an exponential reduction in the number of steps required to solve a problem.

Thinking Fast and Slow: An Approach to Energy-Efficient Human Activity Recognition on Mobile Devices

AI Magazine

Inspired by this model, we propose a framework for implementing human activity recognition on mobile devices. In this area, the mobile app is usually always on and the general challenge is how to balance accuracy and energy consumption. However, among existing approaches, those based on cellular IDs consume little power but are less accurate; those based on GPS/Wi-Fi sampling are accurate often at the costs of battery drainage; moreover, previous methods in general do not improve over time. To address these challenges, our framework consists of two modes: In the deliberation mode, the system learns cell ID patterns that are trained by existing GPS-/Wi-Fi-based methods; in the intuition mode, only the learned cell ID patterns are used for activity recognition, which is both accurate and energy efficient; system parameters are learned to control the transition from deliberation to intuition, when sufficient confidence is gained, and the transition from intuition to deliberation, when more training is needed. For the scope of this paper, we first elaborate our framework in a subproblem in activity recognition, trip detection, which recognizes significant places and trips between them.

Sustainable Policy Making: A Strategic Challenge for Artificial Intelligence

AI Magazine

Each political decision in fact implies some form of social reactions, it affects economic and financial aspects and has substantial environmental impacts. Improving decision making in this context could have a huge beneficial impact on all these aspects. There are a number of Artificial Intelligence techniques that could play an important role in improving the policy-making process such as decision support and optimization techniques, game theory, data and opinion mining and agent-based simulation. We outline here some potential use of AI technology as it emerged by the European Union (EU) EU FP7 project ePolicy: Engineering the Policy Making Life Cycle, and we identify some potential research challenges. They are extremely complex, occur in rapidly changing environments characterized by uncertainty, and involve conflicts among different interests.

Artificial Intelligence for Human-Robot Interaction

AI Magazine

The titles of the seven symposia were Artificial Intelligence for Human-Robot Interaction; Energy Market Prediction; Expanding the Boundaries of Health Informatics Using AI; Knowledge, Skill, and Behavior Transfer in Autonomous Robots; Modeling Changing Perspectives: Reconceptualizing Sensorimotor Experiences; Natural Language Access to Big Data; and The Nature of Humans and Machines: A Multidisciplinary Discourse. The highlights of each symposium are presented in this report. The primary goal of the AI for Human-Robot Interaction symposium was to bring together and strengthen the community of researchers working on the AI challenges inherent to human-robot interaction (HRI). HRI is an extremely interesting problem domain for AI and robotics research. It aims to develop robots that are intelligent, autonomous, and capable of interacting with, modeling, and learning from humans.

Annotating Protein Function through Lexical Analysis

AI Magazine

We now know the full genomes of more than 60 organisms. The experimental characterization of the newly sequenced proteins is deemed to lack behind this explosion of naked sequences (sequencefunction gap). The rate at which expert annotators add the experimental information into more or less controlled vocabularies of databases snails along at an even slower pace. Most methods that annotate protein function exploit sequence similarity by transferring experimental information for homologues. A crucial development aiding such transfer is large-scale, work-and management-intensive projects aimed at developing a comprehensive ontology for gene-protein function, such as the Gene Ontology project.

An Overview of Recent Application Trends at the AAMAS Conference: Security, Sustainability, and Safety

AI Magazine

A key feature of the AAMAS conference is its emphasis on ties to real-world applications. The focus of this article is to provide a broad overview of application-focused papers published at the AAMAS 2010 and 2011 conferences. More specifically, recent applications at AAMAS could be broadly categorized as belonging to research areas of security, sustainability, and safety. We outline the domains of applications, key research thrusts underlying each such application area, and emerging trends. This emphasis of trying to marry theory and practice at AAMAS goes all the way back to the origins of its predecessor conferences, such as the first International Conference on Autonomous Agents (Johnson 1997).

What do made-for-AI processors really do?


Last week, Qualcomm announced the Snapdragon 845, which sends AI tasks to the most suitable cores. There's not a lot of difference between the three company's approaches -- it ultimately boils down to the level of access each company offers to developers, and how much power each setup consumes. Before we get into that though, let's figure out if an AI chip is really all that much different from existing CPUs. A term you'll hear a lot in the industry with reference to AI lately is "heterogeneous computing." It refers to systems that use multiple types of processors, each with specialized functions, to gain performance or save energy.



Last year the UAE got a Minister of Happiness, and now, in another world first, the country has a Minister of Artificial Intelligence – an acknowledgement by the Emirates that these are the technologies that are going to change the world around us, and quickly. H.H. Sheikh Mohammed bin Rashid Al Maktoum, Vice President of the UAE and ruler of Dubai, announced a full cabinet reshuffle today, and as part of that 27-year-old Omar Bin Sultan Al Olama has been announced as the Minister of AI. Al Olama has been working as the Deputy Director of the Future Department for just over a year now, and he has been on the Executive Committee of the World Government Summit since 2014. He has a BBA from the American University of Dubai, and a diploma of excellence and project management from the American University in Sharjah. Well, they plan to use AI to not only streamline costs, but to also bolster education and a desire to learn; to reduce accidents on the roads; and to create savings in the energy industry.



Your next electric car might be fueled up by drone. Amazon was granted a new patent earlier this month that was only recently spotted by Green Tech Media. The patent explains how Amazon drones might one day latch onto an electric vehicle and charge it while it's driving -- a complicated balancing act between the car and the drone. SEE ALSO: Amazon's new Echo Spot is here to replace your alarm clock The drones could be fully autonomous, the patent claims, meaning that they would be able to plan and navigate their own routes without any human assistance. Here's how it would work: An electric vehicle would send a request for fuel on a network to which the drones are connected.