Nowadays, technology is growing and shaping different sectors and fields. The fields need to upgrade the technology as per the needs of the people. From the Education sector to the fashion industry, all things change according to the trends. So, How Machine Learning is helping in shaping space technology? Technology is helping in building a better future for the people by eliminating humans in a time-consuming task.

Click on one of the buttons below to see the agenda of the day. If only Hollywood... (demystifying AI) It is good to invest in AI and even better to understand the uses of AI Stefano Quintarelli, Serial entrepreneur and member of the High-Level Expert Group on Artificial Intelligence of the European Commission

The recent AI revolution was based on a few important pillars: abundance of data, lower costs in storing it, faster computing speed and distributed computing. To date, however, there still are some critical steps that are missing to achieve true continuous intelligence. Continuous intelligence refers to a design in which real-time analytics is seamlessly integrated within a business operation to support decision automation. Data processing can therefore be used to respond real-time to events. In order to achieve true continuous intelligence we are still missing a couple of key elements, in particular explainable AI and better graph analytics.

IBM Corp. today updated its Watson AI tools to help customers eliminate some of the data complexities that prevent them from implementing artificial intelligence-based technologies. The updates are all part of IBM's so-called "Watson Anywhere" initiative that involves scaling AI across any kind of cloud computing platform. Watson Anywhere's main aim is to make data accessible to AI no matter where it's stored. It's built on top of the open source Kubernetes project and enables customers to connect data regardless of where it resides. Watson Anywhere also provides access to a suite of microservices including Watson Openscale and Watson Assistant.

According to PwC, we are on the precipice of $30 trillion transfer of wealth from baby boomers to generation X and Millennials. Of this, US$4.1 trillion are anticipated to change hands over the next ten years alone, specifically within the ultra-high net worth (UHNW) segment . The demographic inheriting this windfall loves technology and embraces change and disruption. Yet at the same time, "wealth management is one of the least tech-literate sectors of financial services". This is a clear red flag for traditional service providers who must evolve to remain competitive.

Machine learning methods based on artificial neural networks are fast becoming the norm with high end programing activities and work. Early restricted to research applications alone deep learning or hierarchical learning is fast being adopted by tech companies in day to day work. We have seen enormous use of machine learning algorithms that run in the backend today powering some of the most famous apps and software we use. It is because of them we are seeing intelligent systems that can predict effectively what will happen next. For instance you are typing and have activated auto keyboard it throws up potential words that you will use next.

AI can be harnessed in a wide range of economic sectors and situations to contribute to managing environmental impacts and climate change.Some examples of application include: AI-infused clean distributed energy grids, precision agriculture, sustainable supply chains, environmental monitoring and enforcement, and enhanced weather and disaster prediction and response. Research by PwC UK, commissioned by Microsoft, models the economic impact of AI's application to manage the environment, across four sectors – agriculture, water, energy and transport. It estimates that using AI for environmental applications could contribute up to $5.2 trillion USD to the global economy in 2030, a 4.4% increase relative to business as usual. In parallel the application of AI levers could reduce worldwide greenhouse gas (GHG) emissions by 4% in 2030, an amount equivalent to 2.4 Gt CO2e – equivalent to the 2030 annual emissions of Australia, Canada and Japan combined. At the same time as productivity improvements, AI could create 38.2 million net new jobs across the global economy offering more skilled occupations as part of this transition.

One of the grand challenges of AI is to create general intelligence: an agent that can excel at many tasks, not just one. In the area of games, this has given rise to the challenge of General Game Playing (GGP). In GGP, the game (typically a turn-taking board game) is defined declaratively in terms of the logic of the game (what happens when a move is made, how the scoring system works, how the winner is declared, and so on). The AI player then has to work out how to play the game and how to win. In this work, we seek to extend the idea of General Game Playing into the realm of video games, thus forming the area of General Video Game Playing (GVGP).

Artificial Intelligence (AI) has been the subject of books and movies alike. It's been a growing concern for its ability to be used in political censorship and to create deep fake news, to be misused in the hands of authoritarian governments to track and control citizens, to create a huge potential loss in jobs over time, ushering in Biblical end time prophecies, and the possibility of a Terminator or Matrix type scenario. There's also been concern over probable long-term agendas concerning the technology. However, that doesn't mean we have to throw AI out the window (and with the looks of things, it seems like that would be very hard to do in the future). Artificial Intelligence can have many benefits, especially in the field of digital marketing, which we'll be talking about in this article.

Unfiltering AI is a series of interviews where in we are attempting to make data science and machine learning aspirants understand these fields from the perspective of real data scientists who haven't seen the lime light yet but are as immersed into the subject as well as the industry as those who have. The current state of data science and machine learning in India 2. The right way to become a data scientist 3. The work life of a data scientist 4. What the field is actually about and a lot more Data Science and machine learning gossip! Do comment below and drop in a question that you would like to ask a real life data scientist. For further updates on data science and machine learning, follow us on Facebook: https://www.facebook.com/skillathon.co/