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3 Challenges to the Universal Adoption of AI


The use of connected smart devices is growing rapidly, but they are not yet everywhere. There are 3 challenges to the universal adoption of AI. As you may have already realized, AI has influenced your life. And its impact is only going to grow from here. Achieving a future of ubiquitous AI could be life-changing.

Artificial Intelligence is Key: Why the Transition to Our Future Energy System Needs AI


On any given day, the electric power industry's operations are complex and its responsibilities vast. As the industry continues to play a critical role in supporting global climate goal challenges, it must simultaneously support demand increases, surges in smart appliance adoption, and decentralized operating system expansions. Behind the scenes, there's the power grid operator, whose role is to monitor the electricity network 24 hours per day, 365 days per year. As a larger number of lower capacity systems (such as renewables) come online and advanced network components are integrated into the grid, generation becomes exponentially more complex, decentralized and variable, stretching control room operators to their limits. More locally, building owners and controllers (Figure 1) are being challenged to deploy grid-interactive intelligent elements that can flexibly participate in grid level operations to economically enhance grid resiliency (while also saving money for the building owner).

IoT is drastically changing the world for the better.


IoT is drastically changing the world for the better. There was a time when internet connectivity was available only on phones and computers. In the past decade, this focus has shifted to all technologies. Gradually, we are seeing the development of devices that connect to the internet. All these devices collect and share data to make our lives easier. You must know what IoT is by now, but for general understanding IoT is a broad umbrella.

Tight Regret Bounds for Noisy Optimization of a Brownian Motion Machine Learning

We consider the problem of Bayesian optimization of a one-dimensional Brownian motion in which the $T$ adaptively chosen observations are corrupted by Gaussian noise. We show that as the smallest possible expected simple regret and the smallest possible expected cumulative regret scale as $\Omega(1 / \sqrt{T \log (T)}) \cap \mathcal{O}(\log T / \sqrt{T})$ and $\Omega(\sqrt{T / \log (T)}) \cap \mathcal{O}(\sqrt{T} \cdot \log T)$ respectively. Thus, our upper and lower bounds are tight up to a factor of $\mathcal{O}( (\log T)^{1.5} )$. The upper bound uses an algorithm based on confidence bounds and the Markov property of Brownian motion, and the lower bound is based on a reduction to binary hypothesis testing.

Artificial intelligence and machine learning face off with new cybersecurity threats


If somebody hacked communications to grid-connected devices and interrupted a demand response (DR) event, peak demand might not be cut, capacity prices could spike and that somebody could make a lot of money. Because of the fast-rising number of grid-connected devices in DR programs like smart thermostats and water heaters and the even faster-rising number of smart phones and other Internet technologies through which customers communicate with DR programs, market manipulations like that are possible, cybersecurity experts from the Electric Power Research Institute (EPRI) told the Demand Response World Forum October 17. It is one of many potential intrusions of communications between utilities and customers with grid connected devices and distributed energy resources (DER), they said. To counter these threats, data analytics experts are using the laws of physics and unprecedented masses of data to find cybersecurity breaches. And their work is leading to machine learning (ML) and artificial intelligence (AI) algorithms which, though only just beginning to find actual deployment, are expected to soon advance the ability to identify patterns to the intrusions and raise the level of protection for critical power systems.