A combination of situational complexity, intractable positions of opposing sides and escalating costs is driving the search for AI-based approaches that could replace humans in resolving legal cases, international disputes and military conflicts. Master of the Rolls and head of civil litigation in England and Wales, Sir Geoffrey Vos, has talked for some time about AI's potential to propose resolutions for humans to ratify. The goal of AI is to develop computer algorithms that replicate the way humans think when processing language, solving problems and analyzing large amounts of data to extract relevant information. The nation is reportedly investing over $400 billion to develop leadership in AI across all domains, and the legal sector is seen as an area where massive efficiencies and financial savings could be achieved by automating significant parts of the judicial process. The third suggested contribution of AI lies in "creating greater inclusivity of mediation processes" -- pulling in the views of a wider cross-section of the affected populations, geographic neighbors of the opposing factions and independent institutions that may have previously played peacekeeping and monitoring roles.

To Be a Machine: Adventures Among Cyborgs, Utopians, Hackers, and the Futurists Solving the Modest Problem of Death (Mark O'Connell). "Flesh is a dead format," writes Mark O'Connell in To Be a Machine, his new nonfiction book about the contemporary transhumanist movement. It's an alarming statement, but don't kill the messenger: As he's eager to explain early in the book, the author is not a transhumanist himself. Instead, he's used To Be a Machine as a vehicle to dive into this loosely knit movement, which he sums up as "a rebellion against human existence as it has been given." In other words, transhumanists believe that technology -- specifically, a direct interface between humans and machines -- is the only way our species can progress from its current, far-than-ideal state.

The following post was written and/or published as a collaboration between Benzinga's in-house sponsored content team and a financial partner of Benzinga. Today's consumer companies and business-to-business (B2B) firms are finding themselves in a fast-paced, competitive race to attract and secure new clients. One approach that's increasingly gaining popularity is customer engagement. Customer engagement entails prioritizing long-term relationships with consumers and B2B clients. It's about working to develop and maintain relationships throughout multiple interactions and across various channels. The research suggests profound benefits for companies that invest in customer engagement solutions.

The Consumer Stocks Package is designed for investors and analysts who need predictions of the best performing stocks for the whole Consumer Industry. It includes 20 stocks with bullish and bearish signals. Package Name: Consumer Stocks Recommended Positions: Long Forecast Length: 1 Year (10/13/20 – 10/13/21) I Know First Average: 210.61% The algorithm correctly predicted 9 out of 10 the suggested trades for this 1 Year forecast. The top performing prediction from this package was GME with a return of 1459.83%.

Many deep learning models pick up objectives using the gradient-descent method. Gradient-descent optimization needs a big number of training samples for a model to converge. That creates it out of shape for few-shot learning. We train our models to learn to achieve a sure objective in generic deep learning models. However, humans train to learn any objective. There are different optimization methods that emphasize learn-to-learn mechanisms.

Putting yourself in a data science role when you've been given the amazing task of building this cutting-edge machine learning solution. You have the data and the motivation but don't know where to start. Is it clear in your mind or you have this rush in your chest but without exactly seeing the path and where to begin? My motivation here is simple: give you, in a straightforward way, where to start and also why each step is important. I remember when I started this journey into the data world, being a little bit crushed under the data science buzz words with the associated technics: it was like being in a storm on a little canoe.

This Commodities Package is designed for investors who need commodity recommendations to find the best performing commodities in the industry. Package Name: Commodities Recommended Positions: Long Forecast Length: 1 Month (9/12/21 – 10/12/21) I Know First Average: 12.32% In this 1 Month forecast for the Commodities Package, there were many high performing trades and the algorithm correctly predicted 10 out of 10 trades. The prediction with the highest return was BCOMCO1T, at 15.53%. BCOMCL2T and BCOMCL3T also performed well for this time horizon with returns of 15.17% and 14.54%, respectively. The package had an overall average return of 12.32% during the period.

At the beginning of the year, I have a feeling that Graph Neural Nets (GNNs) became a buzzword. As a researcher in this field, I feel a little bit proud (at least not ashamed) to say that I work on this. It was not always the case: three years ago when I was talking to my peers, who got busy working on GANs and Transformers, the general impression that they got on me was that I was working on exotic niche problems. Well, the field has matured substantially and here I propose to have a look at the top applications of GNNs that we have recently had. If this in-depth educational content on graph neural networks is useful for you, you can subscribe to our AI research mailing list to be alerted when we release new material.

Data visualization has become synonymous with business intelligence (BI) data dashboarding. But these dashboards have a weakness: They are only as good as the humans–and AI–that interpret it. For businesses to truly unlock their full operational efficiency potential, they must find a better way to translate data, operationalize metadata, and create more visually intuitive ways to build trust and extract value from the data. One of the reasons behind the lack of trust in the data stems from the absence of context around the numbers to make them useful, especially if the data is needed for a range of purposes, viewed by more than the dashboard creator. And more often than not, when the data isn't our own, we tend to distrust it.

Virus transmission from asymptomatic or pre-symptomatic individuals is a key factor contributing to the SARS-CoV-2 pandemic spread. High levels of the SARS-CoV-2 virus have been observed 48–72 hours before symptom onset. D'Haese et al. describe their strategy using an AI model that can predict, with 82% accuracy (negative predictive value 97%, specificity 83%, sensitivity 79%, precision 34%), the likelihood of developing symptoms consistent with a viral infection three days before symptom onset. This model uses a conservative framework, warning potentially pre-symptomatic individuals to socially isolate while minimizing warnings to individuals with a low likelihood of developing viral-like symptoms in the next three days. They asked each participant to 1) wear a smart ring device with sensors that collect physiological measures such as body temperature, sleep, activity, heart rate, respiratory rate, heart rate variability; 2) use a custom mobile health app to complete a brief symptoms diary, social exposure to potentially infected contacts, and measures of physical, emotional, and cognitive workload; as well as the psychomotor vigilance cognitive task (PVT) to measure attention and fatigue twice a day. All data are collected, structured, and organized into the RNI Cloud data lake for analysis.