Are you tired to label your images by hand to work with object detection? Have hundreds or thousands of images to label? Then this project will make your life easier, just create some annotations and let the machine do the rest for you! If you have trouble or doubt check my tutorial on medium. You can also open an issue and I'll hep you!

BofA initiated coverage on Exscientia Plc (NASDAQ:EXAI) with a Buy and $27 price target, implying a 25.5% upside. Analyst Michael Ryskin believes Exscientia stands out from other tech-enabled biotechs, as it uses AI to develop better drugs in a shorter period. Ryskin adds that the company's lead compounds remain in the early Phase 1 trials as the platform validation continues. Goldman Sachs also initiated Exscientia with a Buy and $30 price target, suggesting a 39.5% upside. Analyst Chris Shibutani believes the company is well-positioned to become a "pharmatech leader" in end-to-end artificial intelligence-enhanced drug discovery and development.

Shopify is an innovative technological wave in the digital advancements of e-commerce. Shopify is more than a tool. It is an all-in-one solution that can be used to create or enhance existing online services. Shopify is well-known for its unique online store designs and branding tools. It also offers customized services and applications that allow you to make intelligent decisions based upon real-time reports and other pre-made programs.

Google's parent company Alphabet on Tuesday beat quarterly earnings expectations, raking in $18.9 billion in profit as its online ad engine and cloud services thrived. Google remains a centerpiece of online activity, with offerings such as its search engine, ad marketplace, and YouTube video platform that give it extensive global influence. "This quarter's results show how our (artificial intelligence) investments are enabling us to build more helpful products for people and our partners," said Sundar Pichai, CEO of Alphabet and Google. "As the digital transformation and shift to hybrid work continue, our Cloud services are helping organizations collaborate," he added. The surge in Alphabet's earnings comes as the tech giant faces increased scrutiny from regulators regarding its power.

Predicting how old someone is based only on how they look is incredibly hard to get right, especially in those awkward early teen years. And yet bouncers, liquor store owners, and other age-restricted goods gatekeepers make that quick estimation all the time. This story originally appeared on WIRED UK. Their predictions are often wrong. Now London-based digital identity company Yoti believes its AI-powered age estimation can predict how old someone is if they're aged anywhere from 6 to 60. For the first time, it claims, it can accurately determine whether children are under or over 13, the minimum age many social media firms require their users to be.

The protagonist of the Netflix megahit Squid Game is Seong Gi-hun, an indebted gambler and absentee father who screams, sweats, and strains his way through the very intense experience of watching hundreds of people get straight-up killed--while trying to avoid being killed, and retain some sense of ethics and loyalty, to boot. But we wondered: what was it like to voice Gi-hun in English for the many people who watched Squid Game with the dubbing option turned on? So we asked the voice actor Greg Chun, a veteran of video games and anime who spoke to Slate from his studio in Los Angeles. Our conversation--on the hardest Squid Game scene to dub, the controversy around the Korean-to-English translation, and his time working on Call of Duty--has been edited and condensed for clarity. Rebecca Onion: What was your reaction when you first saw the Squid Game script?

Keeping ethical bias out of artificial intelligence models remains a largely unsolved problem. To prevent bias from developing, a model must be actively monitored from the time it is put into production until the moment it is retired. And, monitoring must be supported with proper governance and operations processes.

The Gaussian mixture model (GMM) is well-known as an unsupervised learning algorithm for clustering. Here, "Gaussian" means the Gaussian distribution, described by mean and variance; mixture means the mixture of more than one Gaussian distribution. Suppose we know a collection of data points are from a number of distinct Gaussian models ( a Gaussian model is described by the mean scalar and the variance scalar for 1-d data and by the mean vector and variance matrix for N-d data), and we can know the probability of each data point belonging to one of the 2 Gaussian models if we know their density functions (as the example shown below). Then, we are able to assign the data point to the one specific model with the highest probability among the Gaussian mixture. From the procedure described above, I believe you have already noticed that there are two most important things in the Gaussian mixture model.

Artificial intelligence (AI) is the ability of computer systems to simulate human intelligence. It has not taken long for AI to become indispensable in most facets of human life, with the realm of cybersecurity being one of the beneficiaries. AI can predict cyberattacks, help create improved security processes to reduce the likelihood of cyberattacks, and mitigate their impact on IT infrastructure. AI can also free up cybersecurity professionals to focus on more critical tasks in the organization. One such concern is AI bias.

Subex helps businesses embrace disruptive changes in the business landscape and succeed with digital trust. The popular crime flick Minority Report from 2002 seemed ahead of its time when it portrayed how a computer system could predict when a person is likely to commit a crime, long before they have even thought about it. However, a decade later, the possibilities imagined by the Tom Cruise blockbuster seem real with the emergence of COMPAS, an artificial intelligence (AI) algorithm that can predict how likely a person is to commit a crime again. The software was widely used across the U.S. until 2016, when a detailed investigation highlighted that the program was biased against a particular race. The problem lay not in the AI algorithm itself but in the data that was fed into it.