Peter leads us in Project Management with over 30 years experience in industry. He is critical of QA procedures and adherence to industry standards with regards to documentations, drawings and calculations. He is also skilled in SWOT analysis for future business planning. Peter has managed a wide range of European Union funded projects under grants by innovateUK. His exposure cuts across nuclear, marine and civil engineering related projects.

A survey of youth 14-18 finds that they are interested in working with emerging technology, but feel unprepared to do so. As a response, IBM has launched three new AI-focused online tools to teach young people about the future of artificial intelligence. IBM's study of the cohort in 13 countries found that 68% of them think that AI will have a major impact on their lives, but half of that number (34%) said they don't feel properly equipped to use the technology that will make a large difference in their futures. "As a company bringing advanced technologies into the marketplace, we have a deep responsibility to ensure that learners have the skills required to participate in the digital economy," IBM said in its announcement of the new educational tools. More than half, 56%, of young people surveyed said they were interested in tech careers, and 60% of those were interested in emerging tech areas like cybersecurity and the cloud. When it comes to any one area of interest, AI dominates with 59% wanting to learn more about it.

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For 23 years, Larry Collins worked in a booth on the Carquinez Bridge in the San Francisco Bay Area, collecting tolls. The fare changed over time, from a few bucks to $6, but the basics of the job stayed the same: Collins would make change, answer questions, give directions and greet commuters. "Sometimes, you're the first person that people see in the morning," says Collins, "and that human interaction can spark a lot of conversation." But one day in mid-March, as confirmed cases of the coronavirus were skyrocketing, Collins' supervisor called and told him not to come into work the next day. The tollbooths were closing to protect the health of drivers and of toll collectors. Going forward, drivers would pay bridge tolls automatically via FasTrak tags mounted on their windshields or would receive bills sent to the address linked to their license plate. Collins' job was disappearing, as were the jobs of around 185 other toll collectors at bridges in Northern California, all to be replaced by technology.

Recently, deep reinforcement learning algorithms have been successfully applied to a wide range of domains ([1], [2], [3], [4]). However, their success relies heavily on dense rewards being given to the agent; and learning in environments with sparse rewards is still a major limitation of RL due to the low sample efficiency of the current algorithms in such scenarios. In sparse rewards settings, the sample inefficiency is essentially caused by the low likelihood of the agent obtaining a reward by random exploration. Recent attempts to tackle this issue revolve around providing the agent an intrinsic reward that encourages exploring new states of the environment, thus increasing the likelihood of reaching the reward ([5], [6], [7]). An alternative way to improve the sample efficiency is curriculum learning ([8]). It consists in first training the agent on an easy version of the task at hand, where it can get reward more easily and learn, then training on increasingly difficult versions using the previously learned policy and finally, training on the task at hand. Its usage is not limited to reinforcement learning and robotics tasks, but also to supervised tasks. Curriculum learning may be decomposed into two parts: 1. Defining the curriculum, i.e. the set of tasks the learner may be trained on.

Cooperative multi-agent decision making involves a group of agents cooperatively solving learning problems while communicating over a network with delays. In this paper, we consider the kernelised contextual bandit problem, where the reward obtained by an agent is an arbitrary linear function of the contexts' images in the related reproducing kernel Hilbert space (RKHS), and a group of agents must cooperate to collectively solve their unique decision problems. For this problem, we propose \textsc{Coop-KernelUCB}, an algorithm that provides near-optimal bounds on the per-agent regret, and is both computationally and communicatively efficient. For special cases of the cooperative problem, we also provide variants of \textsc{Coop-KernelUCB} that provides optimal per-agent regret. In addition, our algorithm generalizes several existing results in the multi-agent bandit setting. Finally, on a series of both synthetic and real-world multi-agent network benchmarks, we demonstrate that our algorithm significantly outperforms existing benchmarks.

This paper seeks to establish a framework for directing a society of simple, specialized, self-interested agents to solve what traditionally are posed as monolithic single-agent sequential decision problems. What makes it challenging to use a decentralized approach to collectively optimize a central objective is the difficulty in characterizing the equilibrium strategy profile of non-cooperative games. To overcome this challenge, we design a mechanism for defining the learning environment of each agent for which we know that the optimal solution for the global objective coincides with a Nash equilibrium strategy profile of the agents optimizing their own local objectives. The society functions as an economy of agents that learn the credit assignment process itself by buying and selling to each other the right to operate on the environment state. We derive a class of decentralized reinforcement learning algorithms that are broadly applicable not only to standard reinforcement learning but also for selecting options in semi-MDPs and dynamically composing computation graphs. Lastly, we demonstrate the potential advantages of a society's inherent modular structure for more efficient transfer learning.

The regret bound of dynamic online learning algorithms is often expressed in terms of the variation in the function sequence ($V_T$) and/or the path-length of the minimizer sequence after $T$ rounds. For strongly convex and smooth functions, , Zhang et al. establish the squared path-length of the minimizer sequence ($C^*_{2,T}$) as a lower bound on regret. They also show that online gradient descent (OGD) achieves this lower bound using multiple gradient queries per round. In this paper, we focus on unconstrained online optimization. We first show that a preconditioned variant of OGD achieves $O(C^*_{2,T})$ with one gradient query per round. We then propose online optimistic Newton (OON) method for the case when the first and second order information of the function sequence is predictable. The regret bound of OON is captured via the quartic path-length of the minimizer sequence ($C^*_{4,T}$), which can be much smaller than $C^*_{2,T}$. We finally show that by using multiple gradients for OGD, we can achieve an upper bound of $O(\min\{C^*_{2,T},V_T\})$ on regret.

We propose VarFA, a variational inference factor analysis framework that extends existing factor analysis models for educational data mining to efficiently output uncertainty estimation in the model's estimated factors. Such uncertainty information is useful, for example, for an adaptive testing scenario, where additional tests can be administered if the model is not quite certain about a students' skill level estimation. Traditional Bayesian inference methods that produce such uncertainty information are computationally expensive and do not scale to large data sets. VarFA utilizes variational inference which makes it possible to efficiently perform Bayesian inference even on very large data sets. We use the sparse factor analysis model as a case study and demonstrate the efficacy of VarFA on both synthetic and real data sets. VarFA is also very general and can be applied to a wide array of factor analysis models.

There's no denying the fact that Deep Learning as we know it, how awesome it is when we can see that with minimal or no human-intervention a job can be done. Since, Machine Learning, Deep Learning is dubbed to be one of the sexiest jobs of the 21st century(hyped?) so there has to be some starting point, a sort of a roadmap that you can follow to reach to the other side. Luckily, we can now approach it relatively easier with modern frameworks like Tensorflow, PyTorch which gives you a high-level interface to build awesome stuff! Let's discuss why you should start with PyTorch. That means line-by-line execution of the code and simultaneous building of the computation graphs just like in python.