In a novel approach that could help reduce carbon emissions, a team of scientists led by Stony Brook's Anatoly Frenkel have described a way to use artificial intelligence (AI) to facilitate the conversion of carbon dioxide (CO2) into methane. By using this method to track the size, structure, and chemistry of catalytic particles under real reaction conditions, the scientists can identify which properties correspond to the best catalytic performance, and then use that information to guide the design of more efficient catalysts. "Improving our ability to convert CO2 to methane would'kill two birds with one stone' by making a sustainable non-fossil-fuel energy source that can be easily stored and transported while reducing carbon emissions," said Anatoly Frenkel, a chemist with a joint appointment at the U.S. Department of Energy's Brookhaven National Laboratory (BNL) and Stony Brook University. Frenkel is a professor of Materials Science in the College of Engineering and Applied Sciences. Frenkel's group has been developing a machine-learning approach to extract catalytic properties from x-ray signatures of catalysts collected as chemicals are transformed in reactions.

After watching dotNetConf videos over the last couple of weeks, I've been really excited to try out some of the new image classification techniques in Visual Studio. The dotNetConf keynote included a section from Bri Actman, who is a Program Manager on the .NET Team (the relevant section is on YouTube from 58m16 to 1hr06m35s). This section showed how developers can integrate various ML techniques and code into their projects using the ModelBuilder tool in Visual Studio – in her example, photographs of the outdoors were classified according to what kind of weather they showed. As well as the keynote, there's another relevant dotNetConf talk by Cesar de la Torre which is also available here on what's new in ML.NET And the way to integrate this into my project looks very straightforward – right click on the project - Add Machine Learning - and choose what type of scenario you want to use, as shown in the screenshot below. I've highlighted the feature that I'm really interested in – image classification.

Email hacking is a commonly used malicious tactic in our increasingly connected world. Cybercriminals compromise email accounts to enter the IT premises of an organization and carry out attacks ranging from fraud and spying to information and identity theft. Without effective security measures to stop email hacks, potential victims can suffer serious consequences. The cyberespionage group Fancy Bear, which specializes in politically motivated attacks, has reportedly targeted the reelection campaign of a U.S. senator earlier this year via credential phishing tactics. Fancy Bear has been garnering headlines since 2015 for targeting political organizations in the U.S., Ukraine, France, Germany, Montenegro, and Turkey.

The TAIAO project (Time-Evolving Data Science / Artificial Intelligence for Advanced Open Environmental Science) will advance the state-of-the-art in environmental data science by developing new machine learning methods for time series and data streams that are able to deal with large quantities of big data in real time, which are tailored to deal with data collected on the New Zealand environment. We will build a new open source framework to implement machine learning on time series data, provide an open available repository with datasets to improve reproducibility in environmental data science, and build capability in fundamental and applied data science, accessible to all New Zealanders. This programme is a new collaboration between the Universities of Waikato, Auckland and Canterbury, Beca and MetService and includes world-leading data scientists, data engineers, and environmental scientists. We will work with regional councils, iwi and co-governance entities to implement the methods we develop to support governance and management decisions with analyses based on large volumes of data that they cannot currently process. We will also make use of our existing strong international collaborations to grow our own data science capabilities and attract top international researchers to work with us on challenging data science problems.

The Center for Human Nature, Artificial Intelligence, and Neuroscience (CHAIN) is pleased to announce their 7th academic seminar as follows. Artificial Intelligence (AI) is nowadays capable of coming up with creative and inventive outputs that until not long ago just human beings were capable to produce. Music, literature and art are already being created by computers and machines. The talk will delve into these burning legal questions and issues, expanding on the challenges AI pose to the authorship and inventorship requirements as well as the legal provisions on originality and inventive step/non obviousness in several jurisdictions, including United States, United Kingdom and the European Union (EU).

As the evolution of the McDonald's drive-through shows, the internet shopping experience, with its recommendation algorithms and personalization, is increasingly shaping the world of brick-and-mortar retail, as restaurants, clothing stores, supermarkets and other businesses use new technology to collect consumer data and then deploy that information to encourage more spending. At some stores, Bluetooth devices now track shoppers' movements, allowing companies to send texts and emails recommending products that customers lingered over but did not buy. And a number of retailers are experimenting with facial-recognition tools and other technologies -- sometimes known as "offline cookies" -- that allow businesses to gather information about customers even when they are away from their computers. In the restaurant world, the increasingly popular food-delivery apps have produced a slew of customer data. But much of that information is controlled by third-party technology companies rather than by the restaurants themselves, underlining the importance of tech expertise as the industry grows more competitive. "A lot of the restaurant chains, the larger ones that have the cash and the clout and the depth, are really turning into quasi-technology companies," said Michael Atkinson, who runs Orderscape, a company that provides voice-ordering technology.

From small-scale home systems that take the guesswork out of growing for beginners to commercial operations and predictive software, the technology has the ability to change how the world grows, sees and uses cannabis, from hemp and CBD to marijuana, along with the other crops we're more accustomed to like cucumbers and tomatoes. "Right now, it's kind of an obscure topic," said Nathaniel Morris, founder of William Bond Ai in Ontario, which uses AI to train machines to grow cannabis. "It's not going to be obscure for long. Morris said AI imaging can find impurities in the plants, like mold, or a male plant that can impact the entire crop, better than the human eye, once trained to do so by an expert. The technology isn't really new -- but adapting it to fit the needs of cannabis growers is.

If you were hoping for one, definitive answer to this question, you may need to readjust your expectations. The truth is in the nuance. Every subscriber, and their relationship with your company, is unique. What's more is that every person behaves differently throughout their day. We all have different schedules, priorities, and distractions that often dictate when and where we check our email. If there was some magical time to send email, then your subscribers' (as well as your own) inbox would become flooded at that time every week. A better question to ask would be, "is there a best time for our company to send email given what we know about our subscribers?" Like all marketing channels, we must take specific steps to understand what works best for our audience. Some marketers prefer to use a test-driven approach for learning this, while others use buyer personas and qualitative feedback to optimize send time. There are even a few tools that use artificial intelligence to look at past email engagement, and customize future send time for you. Rather than try to discover individual best times, we asked marketers from the Databox Partner Program about the strategies they use to find the best time for any sales or marketing email. We grouped the responses by strategy so you can learn a range of ways to execute each one. Some of the participants even shared why they do not believe send time is a major factor, and instead focus on other parts of the email. Some tools can change the send time of each individual email based on past engagement history. Here's how this works- if you do not already have extensive email engagement data, the tool will send your emails at randomized times over the course of a few weeks to build a profile. Then, the tools will begin to personalize the send time based on past opens and clicks.

Coined the fourth industrial revolution, the advancement of artificial intelligence and machine learning brings interesting discussion to the table. Because AI is so comprehensive and covers several industries, we find ourselves asking obscure questions such as "Do we need to legalize predictive AI policing?" With these questions arising, the key one that remains unanswered surrounds ethics. How do we ensure that AI technologies are ethically designed? To answer this question, there are essentially four aspects that dictate the result: the dilemma, the impact, adoption, and institutionalization.

One key to solving Alabama's prison crisis could lie in artificial intelligence. The University of Alabama in Huntsville is leading the charge. The artificial intelligence technology would be used through an app. Parolees would be required to download it, and it would give resources to the parolee and parole officer. The man behind this idea believes it is his way of serving a community he has once dealt with.