If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
This article is the first in a short series of pieces that will recap each of the day's talks for the benefit of those who weren't able to travel to DC for our first conference. Dr. Sephus came to AWS via a roundabout path, growing up in Mississippi before eventually joining a tech startup called Partpic. When asked, she identified access as the biggest barrier to the greater use of AI/ML--in a lot of ways, it's another wrinkle in the old problem of the digital divide. A core component of being able to utilize most common AI/ML tools is having reliable and fast Internet access, and drawing on experience from her background, Dr. Sephus pointed out that a lack of access to technology in primary schools in poorer areas of the country sets kids on a path away from being able to use the kinds of tools we're talking about. Dr. Sephus said that AWS has been hiring sociologists and psychologists to join its tech teams to figure out ways to tackle the digital divide by meeting people where they are rather than forcing them to come to the technology.
Customer relationship management (CRM) software is a critical component of any sales or marketing team's lead generation, nurturing, and converting strategies. It automates tedious processes, like creating groups of leads to email, which removes some of the manual work from marketers. CRM software also allows teams to design their own workflows, customized for their team's operations. It helps them to store and view information that would otherwise be scattered between programs and papers. A navigable CRM platform with plenty of features for any customer-facing teams should be a priority for all businesses.
Speed has always been a critical success factor in winning wars on the battlefield. You need to move troops faster, reach targets more quickly, and strike with speed and precision. However, what is often not talked about is how the speed with which decisions are made plays a role in claiming victory. Alexander the Great's success on the battlefield is often credited to the rapid decision-making capabilities of his armies. Enabled by trust and a decentralized command structure, his troops were able to beat their enemies by "out-decisioning" them.
InstaDeep is an EMEA leader in delivering decision-making AI products. Leveraging their extensive know-how in GPU-accelerated computing, deep learning, and reinforcement learning, they have built products, such as the novel DeepChain platform, to tackle the most complex challenges across a range of industries. InstaDeep has also developed collaborations with global leaders in the AI ecosystem, such as Google DeepMind, NVIDIA, and Intel. They are part of Intel's AI Builders program and are one of only 2 NVIDIA Elite Service Delivery Partners across EMEA. The InstaDeep team is made up of approximately 155 people working across its network of offices in London, Paris, Tunis, Lagos, Dubai, and Cape Town, and is growing fast.
The Ecoculture company will build the fourth stage of a greenhouse complex for the production of tomatoes and cucumbers in the Bobrovsky district of the Voronezh region. The area of the new greenhouse will be 40 hectares, and investments in the project will amount to 15 billion rubles. This will complete the implementation of the investment project launched in 2018 to create Europe's largest modern greenhouse facility. To date, three stages of greenhouses with a total area of 60 hectares and a cost of 20 billion rubles have been put into operation. The production processes in greenhouses are controlled by a central computer that controls the air temperature and carbon dioxide content in the room, regulates watering, supplementary lighting, fertilizing with minerals, and ventilation.
On May 17, two Toulouse-based institutes, the IRT Saint Exupéry and the IUCT-Oncopole, a European center of expertise in oncology, signed a partnership focused on artificial intelligence. The aim of this partnership is to pool cutting-edge skills around AI-based research projects designed to improve prevention, diagnosis and care in oncology, particularly by predicting therapeutic effectiveness. Two of these projects are already at an advanced stage. The Saint Exupéry Institute of Technological Research aims to accelerate scientific and technological research and transfer to the aeronautics and space industries for the development of reliable, robust, certifiable and sustainable innovative solutions. A private research foundation supported by the French government, the IRT's mission is to promote French technological research for the benefit of industry and to develop the ecosystem of the aeronautics, space and critical systems sectors by providing access to its research projects, technological platforms and expertise.
They all had some effect, surely. Could I have done it without them? Hang on, what *is* the it that I wouldn't have done? Real life usually lacks counterfactuals. I sense this topic could add some spice to the discussions of those who have been asking about the role of psychoactive substances in art since time immemorial, though the AI component adds nothing fundamentally new.
Since their introduction in 2017, transformers have become the go-to machine learning architecture for natural language processing (NLP) and computer vision. Although they have achieved state-of-the-art performance in these fields, the theoretical framework underlying transformers remains relatively underexplored. In the new paper A Probabilistic Interpretation of Transformers, ML Collective researcher Alexander Shim provides a probabilistic explanation of transformers' exponential dot product attention and contrastive learning based on distributions of the exponential family. An oft-proposed explanation for transformers' power and performance is their attention mechanisms' superior ability to model dependencies in long input sequences. But this doesn't directly address how and why transformer architecture choices such as exponential dot product attention outperform the alternatives.