When I met my husband, who happens to be white, he told me that he was always seeing women with blonde hair on Tinder and he's not really into blondes. No matter how many times he had swiped left on blondes, the algorithms were always recommending them to him, presumably because pop culture dictates that white men prefer blondes. Luckily for us, the algorithms' tendency to stack blonde women in his swipe deck worked out in our favor because I'm a black woman who, at the time, had blonde hair. In nearly 10 years of swiping through profiles on Tinder, Bumble, Hinge, and OkCupid, I learned that dating apps can provide pathways for finding friendship, adventure, romance, and sometimes, love. But there was one aspect of dating app culture that I couldn't ignore because it was often the first thing matches wanted to talk about: race.

Use algorithms to debug code, maximize revenue, schedule tasks, and create decision trees Measure the efficiency and speed of algorithms Generate Voronoi diagrams for use in various geometric applications Use algorithms to build a simple chatbot, win at board games, or solve sudoku puzzles Write code for gradient ascent and descent algorithms that can find the maxima and minima of functions Use simulated annealing to perform global optimization Build a decision tree to predict happiness based on a person's characteristics Build a decision tree to predict happiness based on a person's characteristics

The Israeli military is using AI-powered detection system that lets soldiers see through walls before attacking. Designed in part with Camero-Tech, Xaver 1000 uses algorithms to track targets behind an obstacle, which are then displayed on a screen fitted in the center of the device. Xaver 1000, which users place directly on the wall, produces such high resolution displays that users can determine if a person is sitting, standing or lying down. The system is also capable of providing measurements of targets and determining if the image is of an adult, child or animal, allowing soldiers or police officers to know what they are up against on the other side of the wall. The device is designed like a diamond with four flaps that open outward.

Machine learning algorithms are behind almost every artificial intelligence technology advancement and application on the market in today's world .

Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. As long as coding and programming are used, algorithms will be at the heart of these technologies, defining what they do and how they do it.

While considerable advances have been achieved in our battle against the coronavirus, new mutations of COVID19 continue to emerge and could threaten public health. To prevent further severe pandemics, researchers from the University of Copenhagen and the immunotherapy company Evaxion have teamed up to develop a new AI tool that can more quickly and effectively predict how different protein elements can be assembled to increase the likelihood of coronavirus protection. Their tool, BIFROST, is a computer model that uses algorithms to put together virus proteins that are most likely to be included in a vaccine, explains Christian Thygesen, an industrial Ph.D. Thygesen has developed the model together with Evaxion and Associate Professor Thomas Hamelryck in the Deep Probabilistic Programming group at the University of Copenhagen's Department of Computer Science. "For a vaccine to be effective, the body must be able to produce antibodies against viruses. It does so if it recognizes dangerous proteins--such as coronavirus spike proteins. With BIFROST, we use algorithms to prioritize the parts of viral proteins that we already know can stimulate an immune response, so that we can assemble them in a way that is most likely to work in a vaccine," says Christian Thygesen.

Regulatory bodies around the world increasingly recognize that they need to regulate how governments use machine learning algorithms when making high-stakes decisions. This is a welcome development, but current approaches fall short. As regulators develop policies, they must consider how human decisionmakers interact with algorithms. If they do not, regulations will provide a false sense of security in governments adopting algorithms. In recent years, researchers and journalists have exposed how algorithmic systems used by courts, police, education departments, welfare agencies and other government bodies are rife with errors and biases.

Advancements in technology are changing the world at a higher rate than people ever thought. It has changed how people do their things, including how they communicate or interact. Though many technologies have come and gone, there's one technology that seems to hold the future of technologies. Machine learning technology has been in use for quite some time, and all indications show that this technology might remain relevant for the foreseeable future. With machine learning, people build systems that allow computers and computerized devices to learn how to do things themselves rather than teach them how to perform those tasks.

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Artificial intelligence (AI) continues to hold its title as the top buzzword of enterprise tech, but its appeal is well-founded. We now seem to be shifting from the era of businesses simply talking about AI, to actually getting hands-on, exploring the ways it can be used to tackle real-world challenges. AI is increasingly providing a solution to problems old and new, then again, while the technology is proving itself incredibly powerful, not all of its potential is necessarily positive. Here, we explore some of the more edge-case applications of AI taking place this year. Advances in deep-learning and AI continue to make deepfakes more realistic.