Fukuoka – A driving school in Onojo, Fukuoka Prefecture, has started using an artificial intelligence-based system on a trial basis to evaluate students' driving skills. It is the first time for a driving school in Japan to introduce such a system for evaluating skills. The AI technology analyzes a variety of data, including the route the vehicle traveled and its speed, as well as the movements of the driver's eyes. Such a system is expected to reduce instructors' workload and ensure appropriate evaluation amid increasing demand for driving lessons for elderly drivers. This month, Minami Fukuoka Driver's School, operated by Onojo-based Minami Holdings, introduced the AI-based system, developed jointly by Tokyo-based Tier IV Inc. and Brain IV Inc., based in Nagoya, both of which develop systems for autonomous driving.

The Swiss Federal Institute of Technology in Zurich (ETH) has opened a new research center for Artificial Intelligence.

We may think that when we speak to another person, the sound of their voice is the most important way we understand them. But masks are teaching us that this might not be the case. In fact, a large part of verbal communication involves reading the context of what a person is saying – analysing their facial expressions, hand gestures, tone of voice – and it also means reading their lips. You may not have realized that you read lips, until the point when everyone's face was covered by a mask during the Covid pandemic. Suddenly, it has dawned on humans that we ALL read lips, to some extent.

"We are laser-focused on continuous improvements to customers' experience across our stores. By leveraging Everseen's Visual AI and machine-learning technology, we're not only able to remove friction for the customer, but we can also remove controllable costs from the business and redirect those resources to improving the customer experience even more." This post was inspired by a recent Kroger article announcing the deployment of visual artificial intelligence (AI) in 2500 stores and new IHL Group edge computing research. Multiple technological trends have been converging for some time and their combination is leading to transformative store operations improving solutions. By 2021, one billion video cameras will be deployed around the world.

The Presidency of the Council issued presidency conclusions on the charter of fundamental rights in the context of artificial intelligence and digital change. The text was supported or not objected to by 26 delegations. These conclusions are designed to anchor the EU's fundamental rights and values in the age of digitalisation, foster the EU's digital sovereignty and actively contribute to the global debate on the use of artificial intelligence with a view to shaping the international framework. The Presidency conclusions focus on a fundamental-rights based approach to artificial intelligence and provide guidance on dignity, freedoms, equality, solidarity, citizens' rights and justice.

Technology keeps on evolving as human problems get more and more complex. We are seeing an unprecedented volume of data being generated in the modern world. Traditional solutions have outlived their utility. What we really need is super intelligent answers to our ever complex problem. Artificial intelligence seems to be the only answer.

The past few years has seen great advancement in the world of generative models (GANs), becoming one of the most promising approaches toward collecting all of the easily accessible open-source information available and using it to develop models and algorithms to analyze and understand. The below blog brings together a general explanation of GANs, alongside the newest papers, video presentations, application methods and more. For a model to be "Generative" must fit in a class of statistical models which contrast against discriminative models. Simply, a generative model is one that can generate new data after learning from the dataset. Therefore, "Generative" describes a class of statistical models that contrasts with discriminative models.

Almost every one of us was introduced to this field of automation, with jargons like Artificial Intelligence(AI), Machine Learning (ML), Deep Learning (DL), Data Science/Analytics, etc., which seemed like magic at first sight. The backbone of the course'Data Science' and those magical terms rests on these below topics: Note: The coursework I did in Machine Learning & Deep Learning was based on python, but there are alternatives like R, MATLAB, etc. but python is the most popular choice. Additionally, problem-solving or standard coding for questions is something students are familiar with throughout academia, but scripting the code in a refined, readable manner is very important when you are writing code for projects especially the ones in production. It's because when you are collaborating on a project, your team members need to be comfortable with your code at first sight. This helps them to improve your code and highly increases the efficiency of your project work.

The next generation of artificial intelligence is built upon the core idea revolving around "data privacy". When data privacy is a major concern and we don't trust anyone withholding our data we can turn to federated learning for building privacy-preserving AI by building intelligent systems privately. Federated learning is about moving computations to data. Where a globally shared model is bought to where the data is e.g. By moving the model to the device, we can collectively train a model as a whole.

Artificial Intelligence is playing an ever more important role in business. Every year, we see a fresh batch of executives implement AI-based solutions across both products and processes. But do you know how they do it? And if you were to try the same, would you know how to achieve the best results? By the end of this article, you will -- you'll see precisely how you can use AI to benefit your entire operation. As is crucial with everything AI, let's start by grounding ourselves in data.