The Industrial IoT (IIoT) eco-system continues to recognize the benefits of artificial intelligence (AI) and as a result, we spend large sums of money in AI work where we may be disappointed with the results. To be successful, we have learned that deploying AI in an IIoT applications needs to be an evolution and not a revolution especially when used in a Operational Technology (OT) environment. Today's talk will share our experiences in developing AI based solutions including successes and challenges and then a recommendation how to evolve your IIoT solutions with an AI component. Using AI in IIoT may yield incredible results; however, the right expectations need to be set from the beginning as AI requires new technologies and skills to yield those results. Our objective is to provide insights in leveraging an AI maturity model and methodologies so you can evolve your IIoT solutions over time as real world experience is gained.

For many companies, digitization and automation are the keys to the further development of additive manufacturing. Thus, more and more manufacturers are relying on cloud-based solutions and integrating various algorithms into their 3D printing solutions in order to exploit the full potential of the technology. As a digital process itself, 3D printing is part of Industry 4.0 and thus an important component of an era in which artificial intelligence, such as machine learning, is increasingly being used to optimize the value chain. Artificial intelligence (AI) is able to process a large amount of complex data in a very short time, which is why it is becoming increasingly important as a decision maker. We explain what machine learning is and why this form of AI is helping to shape the future of additive manufacturing.

Edge analytics continues to gain popularity in the market. And as the number, depth, and range of use cases that benefit from edge analytics grows exponentially, technologies like computer vision further accelerate its adoption. However, requirements for successfully implementing a computer vision solution can be complex; the right technologies, software, and skills are needed. Join this session to learn how Intel and TIBCO are collaborating to make it easier to adopt computer vision capabilities to improve and grow your business. As Chief of Staff & Innovation Strategy of TIBCO's Office of the CTO, Jesús Centeno helps large organizations and strategic partners understand the always changing technology landscape.

The rise in the use of advanced analytics, machine learning (ML) and Artificial Intelligence (AI) and the Internet of Things (IoT) today have driven the technology of simulation into the concept of the digital twin. Digital twins are generally defined as a virtual digital model of a physical system that is used to make better decisions about the real world physical system. Digital twins are usually intertwined with sensors and include a two-way interaction between the physical and digital twin. The growth of "smart" IoT devices – devices with an increasing amount of processing or computational capability – results in more use cases where devices perform independent functions or complex system behaviors. These devices must both provide cryptographic trust to participate in network data exchange and be assured they can trust the network and nodes to which they communicate.

The demand for convenient retail is the main force shaping the industry today and both online shops and brick-and-mortar stores strive to offer the best experience to their customers. And while there are many strategies devised to increase customer satisfaction in digital retail, physical stores still struggle with meeting shoppers' expectations. At Abto Software, we have already developed an easily deployable Cashierless Checkout for retail outlets that eliminates queues and ensures the growth of the store attendance. Today we offer yet another retail automation technique: camera-based product recognition technology for smart fridges and intelligent vending machines. Abto Computer Vision engineers have developed an AI-driven grocery product recognition model for vending machines and smart fridges.

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).

On 10th of June, 2019, twenty-two AI researchers, including Andrew Ng and Yoshua Bengio, published a paper on how climate change can be tackled with machine learning. I really enjoyed reading it and I am convinced that the paper as well as the climatechange.ai For that reason i created a series of blog posts and videos which provide a dense summary, listing many of the proposed solutions and linking research work as well as ongoing projects. In the big picture, all solutions aim to reduce greenhouse gas emissions. As my contribution to the global #ClimateStrike week from September 20th to 27th, i will post one chapter (video and blog post) on every working day.