Imagine you are an IT operations manager at a government agency. At a critical period when almost the entire country is trying to access your IT systems, a manhole fire brings your server connections down. You get a call: The system seems to have backed on to a backup connection that is much lower than the regular 10 GB connection to the data center. The help-desk team starts receiving frantic requests from citizens trying to submit their last-minute paperwork. The fallback is slowing down the responses, which in turn is leading to connection timeouts.

Ideally, a machine learning engineer would have both the skills of a software engineer and the experience of a data scientist and data engineer. However, data scientists and software engineers usually come from very different backgrounds, and data scientists should not be expected to be great programmers, nor should software engineers be expected to provide statistical summaries. Nonetheless, a background in machine learning algorithms and how they can be implemented is critical to the machine learning engineer (MLE). An MLE works with different algorithms and applies them to different codebases and settings. Previous experience with software engineering and codebase would provide a very useful foundation for this career field.

Most financial institutions know it's critical to manage the ever-increasing amounts of accessible data, but many miss the potential in using that data in innovative ways. Financial institutions have a plethora of data they can access, either through their own systems or through public sources. However, many can't -- or won't -- exploit the large volumes of data, particularly the "owned" data that an organization holds about customers. This kind of data is typically called customer relationship management data, such as the purchase history tied to app installs, email addresses and postal addresses. Though financial institutions maintain and collect massive volumes of data, many firms are restricted from fully using that data because they are required to comply with stringent regulations around what can and cannot be done with customer data.

What is interesting about this, and what makes it a great example for what is happening in many industries, is that baseball games will still require an umpire. They will remain a critical part of the game, and there is no suggestion that their job will disappear. In this case, AI is therefore helping umpires become better at their jobs, serving as a second set of eyes so they can be more accurate in a particular part of their role. In this article, I want to discuss the role of design thinking in creating these systems where AI works side by side with people, helping them to become better at their jobs. Another great example is with customer service agents, where a company can use bots to answer certain customer queries, but humans are responsible for other situations, such as when more empathy or understanding is required.

It's time for city administrations and local employers to close AI-related skills gaps. This article is part of an MIT SMR initiative exploring how technology is reshaping the practice of management. While there is much discussion of how artificial intelligence will continue to transform industries and organizations, a key driver of AI's role in the global economy will be cities. How cities deal with coming changes will determine which ones will thrive in the future. Many cities have plans to become "smart cities" armed with AI-driven processes and services, like AI-based traffic control systems, to improve residents' lives.

Proteins are biological high-performance machines. They can be found in every cell and play an important role in human blood coagulation or as main constituents of hairs or muscles. The function of these molecular tools is obvious from their structure. Researchers of Karlsruhe Institute of Technology (KIT) have now developed a new method to predict this protein structure with the help of artificial intelligence. This is very difficult to detect, the experiments needed for this purpose are expensive and complex.

The Hawaiian poʻo-uli, a small bird from the honeycreeper family, was first discovered in 1973. Less than half a century later, it disappeared from the planet. Declared extinct in 2018, it is one of almost 700 vertebrate species that have been driven to extinction in the last 500 years. According to a United Nations report issued earlier this year to policymakers, one million species are at risk of extinction: Human actions threaten more plants and animals than ever before. Although the precise number of species on the planet is difficult to calculate, recent estimates put it at around 8.7 million.

We are a Ukraine-based company which means that our parents and grandparents lived in the era of infamous Soviet collective farms, where tractors were considered to be an ultimate technology. For them, a smart farm will sound like a fairy tale. So let it be, a fairy tale of a smart farm. First of all, what is a smart farm? Smart Farming is a concept of farming management using modern Information and Communication Technologies to increase the quantity and quality of products.

Adoption and investment in artificial intelligence and robotic process automation is still in its early growth stage in the healthcare industry, with just half of hospital leaders familiar with the technologies. WHY IT MATTERS These were among the results of a survey of 115 executives at hospital systems and independent hospitals in the United States, conducted by healthcare digitization vendor Olive and market research firm Sage Growth Partners. The study also found that nearly a quarter (23 percent) of health system executives are looking to invest in the two technologies today, and half said they plan to do so within the next two years. The top reasons cited for deploying AI technology included improving efficiency and reducing costs, improving the quality of care and improving patient satisfaction and engagement. While interest in AI and RPA technology is growing, the survey results also indicated that there is a lack of general knowledge as to where to procure the solutions or what vendors offer them, with more than half of survey respondents unable to name an AI or RPA vendor or solution.

Artificial Intelligence (AI) and Machine learning (ML) are starting to feel a lot like the latest celebrity gossip--high on hype and light on substance. You can barely read a technology article or release today without someone espousing the latest AI/ML capabilities. The sales performance management (SPM) space, in particular, is one where the hype of AI/ML has not yet delivered on the hope of providing truly meaningful, actionable intelligence to customers--until now. Organizations must undergo a sales transformation to truly stay ahead in the fast-paced, competitive sales environment. It all comes back to the data, as well as access to vast computing power and readily available AI/ML algorithms.