Researchers from machine learning lab OpenAI have discovered that their state-of-the-art computer vision system can be deceived by tools no more sophisticated than a pen and a pad. As illustrated in the image above, simply writing down the name of an object and sticking it on another can be enough to trick the software into misidentifying what it sees. "We refer to these attacks as typographic attacks," write OpenAI's researchers in a blog post. "By exploiting the model's ability to read text robustly, we find that even photographs of hand-written text can often fool the model." They note that such attacks are similar to "adversarial images" that can fool commercial machine vision systems, but far simpler to produce.

Artificial Intelligence (AI) is all the buzz with everyone looking for ways to leverage AI in some capacity within their organization. Whether you're running a business focused on insurance, construction, or anything in between, the list of companies adding AI or Data Science teams to help their business succeed is growing every day. Unfortunately for those tasked with making the decision to fund these emerging teams, the ROI for AI investments isn't always immediately clear. Sure there are companies that have increased their bottom line by increasing their AI investment, however, their success isn't the norm. According to leading industry analysts it takes 9 months to turn an idea for an AI application into a mature, stable production capability, and that's assuming the idea isn't one of the 47% of AI investments that never make it out of the lab for one reason or another.

In 2013, Peter Ceglinski and Andrew Turton set up their firm, Seabin, with a selfless ambition: "our ultimate goal is pretty simple. It's a world where sea bins are no longer needed for clean up," Ceglinski said, speaking at IBM Think Australia and New Zealand last month. As a report by ZDNet explains, the creators behind Seabin are focusing on building a future where their own product is only used for monitoring the sea, not for cleaning garbage. The cornerstone to this development is artificial intelligence (AI). "What started out as a garbage can has evolved into this global mission focused on data and behavioral change," Ceglinski said at IBM Think.

To celebrate International Women's Day, we take a look back over the past year of AIhub content and highlight some of our favourite articles, interviews, podcasts and videos, by, or featuring, women in the field. Falaah Arif Khan is an engineer/scientist by training and an artist by nature. She is currently Artist-in-Residence at the Center for Responsible AI at New York University. When we interviewed Falaah in 2020 she had just completed her first comic book, Meet AI. She has since teamed up with other AI researchers on other exciting projects.

As Machine Learning infrastructure has matured, the need for model monitoring has surged. Unfortunately this growing demand has not led to a foolproof playbook that explains to teams how to measure their model's performance. Performance analysis of production models can be complex, and every situation comes with its own set of challenges. Unfortunately, not every model application scenario has an obvious path to measuring performance like the toy problems that are taught in school. In this piece we will cover a number of challenges connected to availability of ground truth and discuss the performance metrics that are available to measure models in each scenario.

The US federal government should do more to fund research and facilitate collaboration which helps cities tap the benefits of artificial intelligence (AI) and other emerging technologies, says a new report from non-profit thinktank the Information Technology and Innovation Foundation (ITIF). "Smart cities offer an important opportunity to address both infrastructure needs and strained state and local budgets at the same time," the report says, noting the large revenue shortfalls many cities face due to the pandemic. Cities can use AI in transport, the electrical grid, buildings, city operations and more. Similarly, a 2020 report from Microsoft and PwC found that AI-enabled decarbonisation technologies could reduce the carbon intensity of the global economy. ITIF's research outlines several key challenges to deployment.

Refraction AI, a company developing semi-autonomous delivery robots, today announced that it raised $4.2 million in seed funding led by Pillar VC. Refraction says that the proceeds will be used for customer acquisition, geographic expansion, and product development well into the next year. The worsening COVID-19 health crisis in much of the U.S. seems likely to hasten the adoption of self-guided robots and drones for goods transportation. They require disinfection, which companies like Kiwibot, Starship Technologies, and Postmates are conducting manually with sanitation teams. But in some cases, delivery rovers like Refraction's could minimize the risk of spreading disease.

Researchers have developed a method based on Artificial Intelligence (AI) that rapidly identifies currently available medications that may treat Alzheimer's disease. The method could represent a rapid and inexpensive way to repurpose existing therapies into new treatments for this progressive, debilitating neurodegenerative condition. Importantly, it could also help reveal new, unexplored targets for therapy by pointing to mechanisms of drug action. "Repurposing FDA-approved drugs for Alzheimer's disease is an attractive idea that can help accelerate the arrival of effective treatment -- but unfortunately, even for previously approved drugs, clinical trials require substantial resources, making it impossible to evaluate every drug in patients with Alzheimer's disease," said researcher Artem Sokolov from Harvard Medical School. "We therefore built a framework for prioritising drugs, helping clinical studies to focus on the most promising ones," Sokolov added.

As rates go up, future earnings are discounted more, harming valuations for growth stocks and increasing attention on value stocks that make profits today. Yet with prices fluctuating with supply and demand, the memory business is a highly cyclical industry, and the whole sector has been in a nasty downturn over the past two and a half years, leading to extremely cheap valuations compared with the rest of the technology space. According to management, this is the first time Micron has had technology leadership in both NAND and DRAM at the same time. The last upcycle saw Micron's EPS hit $11.51 in fiscal 2018, so at today's share price near $90, it's possible there's upside ahead, especially if Micron can make a higher cycle peak this time around. Better yet, Applied still trades at just 20 times this year's earnings estimates, quite reasonable for a highly profitable market leader with strong growth prospects.

For a long time, banks have been at the leading edge of utilizing innovation to assist with front-end and back-end activities. It's nothing unexpected that banks are using artificial intelligence and machine learning techniques to help in a plethora of ways. These emerging technologies are way too useful than one can imagine. Digital transformation is incredibly essential given the extraordinary occasions we are in. To modernize banks and heritage business frameworks and policies without interrupting the current framework is one of the significant difficulties.