Headspace Health has acquired Sayana, an AI-powered mental health and wellness company, the company announced on Wednesday. The terms of the deal were not disclosed. Headspace Health says the acquisition of the San Francisco-based company will expand its ability to provide personalized self-care to its users. The acquisition comes as Headspace and Ginger merged last year to form Headspace Health, which is valued at $3 billion. The merger brought Ginger's therapy and coaching offerings together with Headspace's mindfulness and meditation services.

Researchers out of the University of British Columbia have found that artificial intelligence can detect sepsis quicker. According to a UBC news release Tuesday, sepsis is responsible for at least one in five deaths worldwide, including those from severe cases of COVID-19. But experts warn, the life-threatening condition is difficult to detect early. UBC researchers say because sepsis is defined as the body's dysfunctional response to an infection and has a variety of symptoms – including fever, fatigue, hyperventilation and a fast heart rate – it can often appear at first to be from other diseases. "This new technique dissects the dysfunctional immune responses involved in sepsis like never before, providing new insights into the biological processes involved in sepsis of any type, including that from COVID-19," says Arjun Baghela, a UBC graduate student who led the analysis.

The Spanish AI Agency will be responsible for the development, supervision, and monitoring of projects within the framework of the National AI Strategy, as well as the projects promoted by the European Union – in particular those related to the regulatory development of artificial intelligence and its potential uses. Although the specific competences of the Spanish AI Agency have not yet been specified (since the creation of the body must be approved by law), we will keep a close eye on these developments, considering the high penalties foreseen under the AI Regulation and the supervisory powers that could be granted to this new authority.

Anticipate, readiness, preparedness, forward-looking, foresee, envision, and predict. Leaders in business, politics, arts, culture and civic governing all must possess character, traits, and abilities that these words define as well as imply. This is especially true today. When uncertainty brought on by the COVID-19 pandemic cuts widely across societies and the globe. Historically, leaders have prepared by first looking at the past.

This article was published as a part of the Data Science Blogathon. Cloud computing is a technology that uses the computer system resources like cloud storage, computing power, and they manage data on remote servers and access them via the internet. To know more about Cloud computing. In the last 5 years, the demand for cloud computing keeps on increasing day by day. Many new cloud service providers came to the market. One of the most popular cloud services is the Google cloud platform. In this article, we are going to deep dive into the ML pipeline in GCP (Google cloud platform).

Machine learning algorithms are often referred to as "black boxes." Once the data is put into the algorithm, it is not always possible to know exactly how the algorithm will reach the prediction. This can be especially frustrating when problems occur. MIT's new Mechanical Engineering (MechE) course teaches students how to combine data science and physics-based engineering to tackle the "black box" problem. In Class 2.C161 (Modeling and Designing Physical Systems Using Machine Learning), Professor George Barbastathis teaches how mechanical engineers use their unique knowledge of physical systems to check algorithms and create more accurate predictions.

An inclusive design approach that embraces the participation of young users, their parents, and local communities in the life cycle of an Artificial Intelligence project, is critical for children's empowerment and for responsible AI innovation. If children are going to interact with AI systems, for instance by sharing their stories and emotions with a companion robot, their perspectives and preferences must be included in the design process, so that the AI application not only fits their needs but respect their rights. Furthermore, the inclusion of children, their guardians, and other relevant local actors can help ensure that AI systems are fair and non-discriminatory. UNICEF's policy guidance recommends that children should be in a position to use AI products or services, regardless of their age, gender identities, geographic and cultural diversity. Furthermore, most national AI strategies and major ethical guidelines make only cursory mention of children and their specific needs.

Artificial intelligence is one of the most popular fields for job demands among the current tech-savvy generation. Along with its interesting prospects, it is also high paying. The following are the top posts in the AI field with high-paying salaries. Data scientists are in high demand now and the coming years are going to see more data scientists getting hired by top business companies. In keeping with this demand, data scientists can earn a base salary that is 36% higher than most professionals in the analytics industry.

One of the most surprising and fascinating applications of Artificial Intelligence is for sure recommender systems. In a nutshell, a recommender system is a tool that suggests you the next content given what you have already seen and liked. Companies like Spotify, Netflix or Youtube use recommender systems to suggest you the next video or song to watch given what you have already seen or listened to. The idea of build recommender system has surely not been developed yesterday. In 2006 Netflix announced a 1 million dollar reward to the research team able to build the best recommender system possible given some test data.

Machine Learning is the learning in which a machine can learn on its own without being explicitly programmed. It is an application of Al that provide the system with the ability to automatically learn and improve from experience. Here we can generate a program by integrating the input and output of that program. One of the simple definitions of Machine Learning is "Machine Learning is said to learn from experience w.r.t some class of task T and a performance measure P if learners performance at the task in the class as measured by P improves with experiences." Sometimes Artificial Intelligence and Machine Learning are considered as same but they are somehow different from each other.