Over the last year, I have been immersing myself in a lot of Artificial Intelligence research, including reading multiple books on AI and taking an online class from Stanford on the fundamentals of Artificial Intelligence. FYI, this class was taught by an Adjunct Professor at Stanford, Andrew Ng, a co-founder of Coursera.org, All of this study and research has given me a much better understanding of AI, what it can and can't do, and its potential impact on our world. Although I am not an engineer and come from the marketing research side of the tech market, after nearly 40 years dealing with technology at all levels, my depth of understanding of technology and its impact on our world has always been present in my work and research. AI has been around for decades but is even more prevalent in our tech world today.

New carbon emission tracking technology will quantify emissions of greenhouse gas, holding the energy industry accountable for its CO2 output. Backed by Google, this cutting-edge initiative will be known as Climate TRACE (Tracking Real-Time Atmospheric Carbon Emissions). Advanced AI and machine learning now make it possible to trace greenhouse gas (GHG) emissions from factories, power plants and more. By using image processing algorithms to detect carbon emissions from power plants, AI technology makes use of the growing global satellite network to develop a more comprehensive global database of power plant activity. Because most countries self-report emissions and manually compile results, scientists often rely on data that is several years out of date.

Federal prosecutors are also seeking three years of supervised release and an agreed-upon restitution payment of nearly $756,500 to Alphabet Inc's self-driving car company Waymo, according to the court papers filed in the U.S. District Court for Northern District of California. Levandowski's attorneys have asked for 12 months of home confinement for him, with an obligation to perform community service, and a $95,000 fine, the court papers added. "It is, unfortunately, no exaggeration to say that a prison sentence today can amount to the imposition of a serious health crisis, even a death sentence, given the BOP's (Federal Bureau of Prisons) current inability to control the spread of the coronavirus," Levandowski's attorneys wrote. The case stemmed from accusations by Google and its sister company Waymo in 2017 that Uber jump-started its own self-driving car development with trade secrets and staff that Levandowski unlawfully took from Google. Uber issued company stock to Alphabet and revised its software to settle the case, and the Department of Justice later announced a 33-count criminal indictment against Levandowski.

What are the most pressing issues when it comes to ethics in AI and robotics? How will they affect the way we live (and work)? Sooner or later these issues will concern you, whether you work in the field or not. Here we will go through the main ideas contained in the paper Robot ethics: Mapping the issues for a mechanized world, while I add some of my own input. You will not have many answers, but will probably start asking the right questions.

Artificial intelligence has quickly been entering the legal industry since the inclusion of Coronavirus in our daily lives. With its growing presence, lawyers and those who rely on the legal profession to make a living; are concerned with how COVID-19 and AI technologies will disrupt, change, and affect the legal world for years to come.

Experts in Artificial Intelligence (AI) development predict that advances in the development of intelligent systems and agents will reshape vital areas in our society. Nevertheless, if such an advance is not made prudently and critically, reflexively, it can result in negative outcomes for humanity. For this reason, several researchers in the area have developed a robust, beneficial, and safe concept of AI for the preservation of humanity and the environment. Currently, several of the open problems in the field of AI research arise from the difficulty of avoiding unwanted behaviors of intelligent agents and systems, and at the same time specifying what we really want such systems to do, especially when we look for the possibility of intelligent agents acting in several domains over the long term. It is of utmost importance that artificial intelligent agents have their values aligned with human values, given the fact that we cannot expect an AI to develop human moral values simply because of its intelligence, as discussed in the Orthogonality Thesis. Perhaps this difficulty comes from the way we are addressing the problem of expressing objectives, values, and ends, using representational cognitive methods. A solution to this problem would be the dynamic approach proposed by Dreyfus, whose phenomenological philosophy shows that the human experience of being-in-the-world in several aspects is not well represented by the symbolic or connectionist cognitive method, especially in regards to the question of learning values. A possible approach to this problem would be to use theoretical models such as SED (situated embodied dynamics) to address the values learning problem in AI.

Local Interpretable Model-Agnostic Explanations (LIME) is a popular method to perform interpretability of any kind of Machine Learning (ML) model. It explains one ML prediction at a time, by learning a simple linear model around the prediction. The model is trained on randomly generated data points, sampled from the training dataset distribution and weighted according to the distance from the reference point - the one being explained by LIME. Feature selection is applied to keep only the most important variables. LIME is widespread across different domains, although its instability - a single prediction may obtain different explanations - is one of the major shortcomings. This is due to the randomness in the sampling step, as well as to the flexibility in tuning the weights and determines a lack of reliability in the retrieved explanations, making LIME adoption problematic. In Medicine especially, clinical professionals trust is mandatory to determine the acceptance of an explainable algorithm, considering the importance of the decisions at stake and the related legal issues. In this paper, we highlight a trade-off between explanation's stability and adherence, namely how much it resembles the ML model. Exploiting our innovative discovery, we propose a framework to maximise stability, while retaining a predefined level of adherence. OptiLIME provides freedom to choose the best adherence-stability trade-off level and more importantly, it clearly highlights the mathematical properties of the retrieved explanation. As a result, the practitioner is provided with tools to decide whether the explanation is reliable, according to the problem at hand. We extensively test OptiLIME on a toy dataset - to present visually the geometrical findings - and a medical dataset. In the latter, we show how the method comes up with meaningful explanations both from a medical and mathematical standpoint.

Strong engagement operates with the understanding that participants have a mandate to drive change and an influence on the policymaking and the decisions ultimately made. Participants may have different roles, including informing, consulting, involving, collaborating or empowering. It's important designers are clear about the role they want participants to play and the level of influence they'll have. Without that clarity, trust will be lost. Engagement drives a strong "before and after," where discussions are linked to outcomes requested by governments, businesses and the people.

Sometimes it's tempting to think of every technological advancement as the brave first step on new shores, a fresh chance to shape the future rationally. In reality, every new tool enters the same old world with its same unresolved issues. In a moment where society is collectively reckoning with just how deep the roots of racism reach, a new paper from researchers at DeepMind -- the AI lab and sister company to Google -- and the University of Oxford presents a vision to "decolonize" artificial intelligence. The aim is to keep society's ugly prejudices from being reproduced and amplified by today's powerful machine learning systems. The paper, published this month in the journal Philosophy & Technology, has at heart the idea that you have to understand historical context to understand why technology can be biased.

Ever wanted a friend who is always there for you? Someone who will perk you up when you're in the dumps or hear you out when you're enraged? Only, she isn't called Replika. She's called whatever you like; Diana; Daphne; Delectable Doris of the Deep. Gender, voice, appearance: all are up for grabs.