In the age of heightened climate-change awareness and an increasingly urgent transition to low-carbon energy, the days when oil companies such as BP could rely purely on fossil fuels and make their own commercial weather are over, according to the FTSE 100's group head of technology, David Eyton. "For a period of time, being just oil and gas was sufficient," he says. "Now it's not; the world is changing, thank goodness. Most nations are embracing sustainable energy and the array of businesses that we [BP] might participate in is expanding." The role of technology – and of heads of technology – is also changing, he adds.

Employee activism and outside pressure have pushed big tech companies like Amazon, Microsoft and Google promising to slash their carbon emissions. When Microsoft held an all-staff meeting in September, an employee asked CEO Satya Nadella if it was ethical for the company to be selling its cloud computing services to fossil fuel companies, according to two other Microsoft employees who described the exchange on condition they not be named. Such partnerships, the worker told Nadella, were accelerating the oil companies' greenhouse gas emissions. Microsoft and other tech giants have been competing with one another to strike lucrative partnerships with ExxonMobil, Chevron, Shell, BP and other energy firms, in many cases supplying them not just with remote data storage but also artificial intelligence tools for pinpointing better drilling spots or speeding up refinery production. The oil and gas industry is spending roughly $20 billion each year on cloud services, which accounts for about 10% of the total cloud market, according to Vivek Chidambaram, a managing director of Accenture's energy consultancy.

The promise of big data and artificial intelligence is everywhere. And, in all cases, so are the results. One almost gets the impression that there is no problem that cannot be solved with these new technologies. The answer to everything is'big data and artificial intelligence'. Open a web browser and you see advertising tuned to your latest online shopping.

In this May 6, 2019 file photo, Microsoft CEO Satya Nadella delivers the keynote address at Build, the company's annual conference for software developers in Seattle. Microsoft and other tech giants have been competing to strike lucrative partnerships with ExxonMobil, Chevron, Shell, BP and other energy firms. One employee stood up to ask Microsoft CEO Nadella about the ethics of the company's oil and gas contracts at an all-staff meeting in Sept. 2019, and Nadella defended the partnerships. Employee activism and outside pressure have pushed big tech companies like Amazon, Microsoft and Google promising to slash their carbon emissions. When Microsoft held an all-staff meeting in September, an employee asked CEO Satya Nadella if it was ethical for the company to be selling its cloud computing services to fossil fuel companies, according to two other Microsoft employees who described the exchange on condition they not be named.

I just won 1st place out of 4,500 teams in the LANL Earthquake Research competition, sponsored by Los Alamos National Laboratory. Yea, that's the place where they invented the nuclear bomb! This awarded me my third gold medal. Our team's writeup can be found here. You can also see all of the code needed to get first place by clicking here. I learned how to deal with signal data and also how to exploit the Kolmogorov-Smirnov test for regression tasks.

Designing deep learning models for highly-constrained hardware would allow imbuing many edge devices with intelligence. Microcontrollers (MCUs) are an attractive platform for building smart devices due to their low cost, wide availability, and modest power usage. However, they lack the computational resources to run neural networks as straightforwardly as mobile or server platforms, which necessitates changes to the network architecture and the inference software. In this work, we discuss the deployment and memory concerns of neural networks on MCUs and present a way of saving memory by changing the execution order of the network's operators, which is orthogonal to other compression methods. We publish a tool for reordering operators of TensorFlow Lite models and demonstrate its utility by sufficiently reducing the memory footprint of a CNN to deploy it on an MCU with 512KB SRAM.

We experimentally achieve a 19% capacity gain per Watt of electrical supply power in a 12-span link by eliminating gain flattening filters and optimizing launch powers using machine learning by deep neural networks in a massively parallel fiber context.

Spiking neural networks (SNNs) are distributed trainable systems whose computing elements, or neurons, are characterized by internal analog dynamics and by digital and sparse synaptic communications. The sparsity of the synaptic spiking inputs and the corresponding event-driven nature of neural processing can be leveraged by energy-efficient hardware implementations, which can offer significant energy reductions as compared to conventional artificial neural networks (ANNs). The design of training algorithms lags behind the hardware implementations. Most existing training algorithms for SNNs have been designed either for biological plausibility or through conversion from pretrained ANNs via rate encoding. This article provides an introduction to SNNs by focusing on a probabilistic signal processing methodology that enables the direct derivation of learning rules by leveraging the unique time-encoding capabilities of SNNs. We adopt discrete-time probabilistic models for networked spiking neurons and derive supervised and unsupervised learning rules from first principles via variational inference. Examples and open research problems are also provided.

Robotics has proved to be an indispensable tool in many industrial as well as social applications, such as warehouse automation, manufacturing, disaster robotics, etc. In most of these scenarios, damage to the agent while accomplishing mission-critical tasks can result in failure. To enable robotic adaptation in such situations, the agent needs to adopt policies which are robust to a diverse set of damages and must do so with minimum computational complexity. We thus propose a damage aware control architecture which diagnoses the damage prior to gait selection while also incorporating domain randomization in the damage space for learning a robust policy. To implement damage awareness, we have used a Long Short Term Memory based supervised learning network which diagnoses the damage and predicts the type of damage. The main novelty of this approach is that only a single policy is trained to adapt against a wide variety of damages and the diagnosis is done in a single trial at the time of damage.

On 10th of June, 2019, twenty-two AI researchers, including Andrew Ng, David Rolnick and Yoshua Bengio, published a paper on how climate change can be tackled with machine learning. I really enjoyed reading it and I am convinced that the paper as well as the climatechange.ai For that reason i created a series of blog posts and videos which provide a dense summary, listing many of the proposed solutions and linking research work as well as ongoing projects. In the big picture, all solutions aim to reduce greenhouse gas emissions. As my contribution to the global #ClimateStrike week from September 20th to 27th, I will post one chapter (video and blog post) on every working day.