AI, computer vision and machine learning systems proved that machines are better and faster than humans analyzing big data. Today, organizations have large datasets of patient data and insights about diseases through techniques like Genome Wide Association Studies (GWAS). Using AI, healthcare providers can analyze and interpret the available patient data more precisely for early diagnosis and better treatment. Today, it is possible to say whether a person has the chance to get cancer from a selfie using computer vision and machine learning to detect increased bilirubin levels in a person's sclera, the white part of the eye. As the interest in AI in the healthcare industry continues to grow, there are numerous current AI applications, and more use cases will emerge in the future.
Sanmay Das, Professor, Computer Science, is conducting an exploratory study in the use of techniques from artificial intelligence (AI) to improve early screening and the delivery of targeted assistance to households that are at risk of future homelessness and child maltreatment. Das and the other members of the research team seek to develop novel methods for allocation of scarce housing support to at-risk households, taking into account considerations of both overall efficiency and fairness. This work will necessitate novel problem formulation and algorithm development in AI as well as creating new ethical methods for deciding on how to effectively deliver social services while considering the vast complexity of human behavior. Das is collaborating with Patrick J. Fowler, Associate Professor at Washington University in St. Louis, on this project. The researchers will explore the feasibility of using novel algorithmic techniques to inform societal decision-making on the allocation of scarce resources, with the specific goal of improving service system outcomes for both homelessness and child welfare.
There is a popular media narrative that AI will take over teachers. If anything, AI will be a new tool in teachers' toolkits. Teachers spend a good portion of their time reeling with administrative burdens. AI will not replace them but free up their time to focus on what they do best – helping students grow to comprehend the world. ARTiBA explores the impact of Artificial Intelligence in Education, its future, and the ongoing AI and ML innovations in the sector.
Brussels – NATO leaders warned Monday that China's military ambitions pose "systemic challenges" to their alliance, and agreed to enhance ties with Japan and other Asia-Pacific nations to back the rules-based international order. The tough line against Beijing, taken in a communique released after the NATO summit, came as U.S. President Joe Biden rallies allies to counter what he calls autocracies like China and Russia that are challenging an open international order. "China's stated ambitions and assertive behavior present systemic challenges to the rules-based international order and to areas relevant to alliance security," said the communique from the 30-member organization that brings together North American and European countries. The leaders also expressed concerns over what they called China's coercive policies, while pointing out the country's rapid expansion of its nuclear arsenal and criticizing the opaqueness of its military modernization. The communique, meanwhile, named Australia, Japan, New Zealand and South Korea as countries with which NATO plans to strengthen its "political dialogue and practical cooperation" in a bid to promote cooperative security and support the rules-based international order.
Between his mom's place in Manhattan, his dad in Queens, and his high school in the Bronx, Noah Getz is on the subway a lot. It gives him time to read and to think. Our first coronavirus summer was waning, and he'd been wrestling with a weighty science problem: using machine learning to hunt down tiny molecules that may help treat Alzheimer's. Thus far, his AI had been spitting out results that were "almost comically bad." The problem was that the algorithms Getz was using did their best when they had massive amounts of data to sift through and discover patterns in. Getz' data set was far smaller; he was working with one lab at Mount Sinai, not a multinational pharmaceutical company with a galaxy-sized drug library.
With ambitions to establish a network of autonomous trucking routes across the US, transport startup TuSimple is taking some steady and significant steps forward as it proves its technology through trials and expands into Europe. The latest test run for its self-driving trucks involved hauling a load of fresh produce over hundreds of miles across the US, where it demonstrated that it can complete such tasks in a fast and highly efficient fashion. Previously, we've seen TuSimple's Level 4 autonomous trucks use its variety of cameras and sensors to move goods as part of trials for the US Postal Service and shipping giant UPS. This time around, the startup has partnered with fresh produce provider The Giumarra Companies and Associated Wholesale Grocers to explore autonomous trucking's potential in the fresh food industry. The trial started in Nogales, Arizona, where TuSimple's truck was loaded up with fresh watermelons from Giumarra's facility.
Levi's loyalty program, which launched in 2020, has built a customer pool that now includes 5 million members; using AI, this facet of the company's business is more personalized to each client than ever before. A company that has followed a progressive course over its 168-year history, Levi Strauss & Co. has played an important role during revolutionary moments within history. From creating an integrated employment force in the mid–20th century or ensuring greater supply-chain transparency in the 1990s to encouraging United States citizens to vote in 2020, the San Francisco–based denim leader has remained committed to progress. This part of the brand's mission made it a perfect fit for Chief Global Strategy and Artificial Intelligence Officer Katia Walsh, who considers herself to be an unlikely fashion professional but has felt aligned with Levi's principles. As a student journalist growing up in communist Bulgaria, Walsh was reprimanded in school at 15 years old for writing a story that displeased local officials.
AI's potential impact on the U.S. economy could reach into the trillions of dollars, according to a report published this week. Signal AI, which offers a decision augmentation platform infused with AI, interviewed 1,000 C-suite executives in the U.S. for the study. The report found 85% of respondents estimate upwards of $4.26 trillion in revenue is being lost because organizations lack access to AI technologies to make better decisions faster. According to the Signal AI survey, 96% of business leaders said they believe AI decision augmentation will transform decision-making, with 92% agreeing companies should leverage AI to augment their decision-making processes. More than three-quarters of respondents (79%) also noted that their organizations are already using AI technologies to help make decisions.
The Covid-19 pandemic was devastating for many industries, but it only accelerated the use of artificial intelligence across the U.S. economy. Amid the crisis, companies scrambled to create new services for remote workers and students, beef up online shopping and dining options, make customer call centers more efficient and speed development of important new drugs. Even as applications of machine learning and perception platforms become commonplace, a thick layer of hype and fuzzy jargon clings to AI-enabled software.That makes it tough to identify the most compelling companies in the space--especially those finding new ways to use AI that create value by making humans more efficient, not redundant. With this in mind, Forbes has partnered with venture firms Sequoia Capital and Meritech Capital to create our third annual AI 50, a list of private, promising North American companies that are using artificial intelligence in ways that are fundamental to their operations. To be considered, businesses must be privately-held and utilizing machine learning (where systems learn from data to improve on tasks), natural language processing (which enables programs to "understand" written or spoken language) or computer vision (which relates to how machines "see"). AI companies incubated at, largely funded through or acquired by large tech, manufacturing or industrial firms aren't eligible for consideration. Our list was compiled through a submission process open to any AI company in the U.S. and Canada. The application asked companies to provide details on their technology, business model, customers and financials like funding, valuation and revenue history (companies had the option to submit information confidentially, to encourage greater transparency). Forbes received several hundred entries, of which nearly 400 qualified for consideration. From there, our data partners applied an algorithm to identify 100 companies with the highest quantitative scores--and that also made diversity a priority. Next, a panel of expert AI judges evaluated the finalists to find the 50 most compelling companies (they were precluded from judging companies in which they have a vested interest). Among trends this year are what Sequoia Capital's Konstantine Buhler calls AI workbench companies--building of platforms tailored to different enterprises, including Dataiku, DataRobot Domino Data and Databricks.