Booking a badminton court at one of Singapore's 100-odd community centers can be a workout in itself, with residents forced to type in times and venues repeatedly on a website until they find a free slot. Thanks to artificial intelligence (AI), it could soon be easier. The People's Association, which runs the community centers, worked with a government tech agency to build a chatbot powered by generative artificial intelligence to help residents find free courts in the city-state's four official languages. The booking chatbot, which could be rolled out shortly, is among more than 100 generative AI-based solutions spurred by the AI Trailblazers project, launched last year to find AI-based solutions to everyday problems.

A model set of opportunities and obstacles have emerged with the advent of the digital age. Cyber security testing has become increasingly important as organizations look to protect their networks, data, and systems from malicious attacks. As technology continues to evolve and become more sophisticated, artificial intelligence (AI) is tapped up as a way to improve cybersecurity testing. Although, AI has the potential to significantly reduce the time and effort needed to find and fix vulnerabilities, as well as to detect and respond to threats more quickly. AI can also help to identify patterns and uncover hidden threats that may have gone undetected.

The Indian Council of Medical Research (ICMR) has released the country's first Ethical Guidelines for Application of Artificial Intelligence in Biomedical Research and Healthcare, aimed at creating "an ethics framework which can assist in the development, deployment, and adoption of AI-based solutions" in the fields specified. Through this, they hope to make "AI-assisted platforms available for the benefit of largest section of common people with safety and highest precision possible," while also addressing emerging ethical challenges when it comes to AI in biomedical research and healthcare delivery. The document, prepared by the Department of Health Research and ICMR Artificial Intelligence Cell, Delhi, will be updated as and when the need arises, said a senior Health Ministry official. Developed through extensive discussions with experts and ethicists, the guidelines include sections on ethical principles, guiding principles for stakeholders, an ethics review process, governance of AI use, and informed consent. "It [the document] is intended for all stakeholders involved in research on AI in biomedical research and healthcare, including creators, developers, researchers, clinicians, ethics committees, institutions, sponsors, and funding organizations,'' noted Dr. Rajiv Bahl, director-general, ICMR.

This article was published as a part of the Data Science Blogathon. In today's world, where the population is increasing at an alarming rate, food waste has become a major issue. According to recent statistics, one-third of all food produced globally is wasted. This results in a significant loss of resources and contributes to environmental problems such as greenhouse gas emissions. The food waste problem is not only limited to developed countries but is also prevalent in developing countries. The Food and Agriculture Organization (FAO) estimates that food waste generates about 8% of global greenhouse gas emissions.

Digitalization has taken over every walk of life, be it something as simple as buying a flight ticket, booking a movie, or ordering food. We are surrounded by innovations in technology like Additive Manufacturing, 3D Printing, Artificial Intelligence, IoT, Robotics, and more. Today artificial intelligence has worked wonders in arenas of problem-solving, learning, object detection, and others for household, industrial and commercial applications. One of the prime areas where AI is proving its potential for innovations and breakthroughs is electrical safety. AI can help to reduce human intervention and drastically reduce the factor of human error.

The use of artificial intelligence (AI) has become increasingly common in many different industries and fields, from healthcare to finance to transportation. While the potential benefits of AI are vast and numerous, it's important to also consider the potential drawbacks and negative uses of this technology. One area where AI is increasingly being utilized is in the realm of cybercrime. One way that AI is used in cybercrime is through the development of sophisticated malware. This type of software is designed to infect a computer or network without the user's knowledge and can cause significant damage or disruption.

We're getting Prabakaran Chandran on board to lead an interactive DataHour session with us. He has been working with Mu Sigma, a prestigious company as a Data and Decision Scientist that specializes in problem-solving, since 2019. He is skilled in SQL, Python, R, Advanced Analytics, and Statistics. In the fields of computer vision, natural language processing, and deep learning, he worked with a team of two people to develop AI-based solutions for Fortune 500 companies. He will be explaining Causal Inference and demonstrate how it may be applied to a specific use case in Python.

Google Cloud and power company Engie announced today the launch of a new partnership aimed at optimizing the value of Engie's wind portfolio and accelerating wind energy development through the development of a new artificial intelligence-based (AI) solution. The new solution aims to address some of the key challenges for wind power developers and operators, driven by the complexity of the short-term power markets and the unpredictable nature of wind production. Utilizing advanced machine learning algorithms applied to vast amounts of data from disparate sources, the solution will predict how much wind power should be sold on which power market and at what price. The companies highlighted the potential far-reaching impact of the new project, with hundreds of gigawatts of wind farms around the world potentially benefiting from improved AI-based forecasting. "At Google Cloud, we believe that more accurate data and predictions of wind power production will be valuable to electricity grids, creating benefits for consumers and making wind more competitive with fossil fuels. We are delighted to work with ENGIE on this project, which can accelerate Europe's clean energy transition, while laying the groundwork for wind farms around the world to benefit from improved forecasting via Artificial Intelligence."

We have built the KONUX Predictive Maintenance System for Rail Switches, the first AI-based solution for the rail industry. As a Data Scientist (with focus on Statistics/Engineering) at KONUX, you seek mathematical robustness in solutions, love to code and get your hands dirty with large volumes of complex raw data from which you will seek to derive meaningful and actionable insights. Ideal candidates are self-motivated, self-directed and are happy to learn and adapt quickly. They will also be able to explore a wide range of statistical models and modeling techniques to understand and effectively break-down hard problems. KONUX scientists and engineers appreciate the difference between explaining and fitting statistical models, the importance of good metrics, and the tradeoff between exploration and exploitation.

Is it finally the year for the rise of the machines? Until not too long ago, AI was just an overused marketing term. Many software vendors who sold solutions based on algorithms and fancy regular expressions branded their stuff as artificial intelligence, even though it wasn't. Times have changed, and the market is--in a helicopter view--divided into two camps: vendors who use a predefined AI framework and vendors who create their own. I'm not looking into the pros and cons of each, but what does this mean for the users?