Category: AI

The patent was granted, oddly enough, for the creation of a fractal-based food and beverage container that might improve on currently available designs like the one pictured.

Intellectual property (IP) officials in South Africa have made history in a landmark decision to award a patent that names an artificial intelligence (AI) as the inventor.

The patent—which was filed by an international team of lawyers and researchers led by the University of Surrey’s, Professor of Law and Health Sciences, Ryan Abbott —is for a food container based on fractal geometry. This container was designed and created by an AI called DABUS (“device for the autonomous bootstrapping of unified sentience”).

Historically, an “inventor” of a patent had to be a human being, although the ownership of that patent is commonly given to the company that employs the inventor. While patent law in many jurisdictions is very specific in how it defines an inventor, the DABUS team is arguing that the status quo is not fit for purpose in the Fourth Industrial Revolution.

Healthcare facilities are looking to artificial intelligence to optimise operational performance at a time when rising demand for their services is stretching resources and limiting the patient experience.

Improving operational efficiency has emerged as a priority for healthcare facilities as they seek predictive ways to manage and allocate resources at a time of ever-increasing demand for their services.

Many of them are now turning to AI as a key enabler of a more progressive approach, helping them to plan their logistical responses based on the latest data – and maintain their focus on delivering end-to-end patient care of the highest quality.

By Dipayan Mitra

Scientists have developed a new artificial intelligence technology that can spot shipwrecks from the ocean surface and also from the air. 

The University of Texas collaborated with the United State Navy’s underwater archeology branch to develop this new artificial intelligence software capable of detecting shipwrecks with an accuracy rate of 92%. 

The newly developed computer model is now ready to be deployed in order to identify unmapped shipwrecks on the coasts of the United States and Puerto Rico. The artificial intelligence algorithm was fed with images of shipwrecks and underwater topology to enable it to recognize unknown wrecks. 

The platform uses images from publicly available databases of pictures collected from various parts of the globe and the National Oceanic and Atmospheric Administration’s database of shipwrecks. It also uses lidar and sonar-based imageries of the seafloor to carry out its operations more accurately. 

Artificial intelligence (AI) is proving very adept at certain tasks – like inventing human faces that don’t actually exist, or winning games of poker – but these networks still struggle when it comes to something humans do naturally: imagine.-

Once human beings know what a cat is, we can easily imagine a cat of a different color, or a cat in a different pose, or a cat in different surroundings. For AI networks, that’s much harder, even though they can recognize a cat when they see it (with enough training).

To try and unlock AI’s capacity for imagination, researchers have come up with a new method for enabling artificial intelligence systems to work out what an object should look like, even if they’ve never actually seen one exactly like it before.

“We were inspired by human visual generalization capabilities to try to simulate human imagination in machines,” says computer scientist Yunhao Ge from the University of Southern California (USC).-

“Humans can separate their learned knowledge by attributes – for instance, shape, pose, position, color – and then recombine them to imagine a new object. Our paper attempts to simulate this process using neural networks.”

A new “red team” will try to anticipate and thwart attacks on machine learning programs.

THE PENTAGON SEES  artificial intelligence as a way to outfox, outmaneuver, and dominate future adversaries. But the brittle nature of AI means that without due care, the technology could perhaps hand enemies a new way to attack.

The Joint Artificial Intelligence Center, created by the Pentagon to help the US military make use of AI, recently formed a unit to collect, vet, and distribute open source and industry machine learning models to groups across the Department of Defense. Part of that effort points to a key challenge with using AI for military ends. A machine learning “red team,” known as the Test and Evaluation Group, will probe pretrained models for weaknesses. Another cybersecurity team examines AI code and data for hidden vulnerabilities.

Machine learning, the technique behind modern AI, represents a fundamentally different, often more powerful, way to write computer code. Instead of writing rules for a machine to follow, machine learning generates its own rules by learning from data. The trouble is, this learning process, along with artifacts or errors in the training data, can cause AI models to behave in strange or unpredictable ways.

“For some applications, machine learning software is just a bajillion times better than traditional software,” says Gregory Allen, director of strategy and policy at the JAIC. But, he adds, machine learning “also breaks in different ways than traditional software.”

By Poornima Apte

There’s Netflix for movies. Stitch Fix for clothes. Hungryroot, an AI-powered delivery service, hopes to occupy a similar niche for online groceries in the United States.

The recommender system uses a collaborative filtering, supervised learning model to match consumer preferences to foods. Customers answer questions about their dietary habits, the kinds of foods they (and family members) like, the family size, budget, and more. On a weekly basis, the Hungryroot algorithm predicts the groceries the customer might like. Once the customer approves the list, a box ships from one of three Hungryroot locations. Customers also receive a set of recipes, also predicted by the algorithm, that use the week’s ingredients.

Neil Saunders, the managing director of GlobalData’s retail division, has seen grocery retailers of all stripes lean into AI as a way of better forecasting demand. “With the disruption from the pandemic and more people buying groceries online, demand forecasting has become increasingly difficult for retailers and AI can help them make sense of the data and make more accurate decisions about what to stock,” Saunders says.

Retailers have turned to AI to replace photoshoots and predict what people will want to buy and wear in the future

Julie Bornstein spent two years quietly building AI shopping app THE YES to launch it in March 2020. Then the pandemic struck – and changed what people were wearing. “Right now, we’re in a heavy comfort zone,” Bornstein says. The pandemic has meant demand for tracksuit bottoms and work-from-home clothing is high. But as vaccines allow people more freedom, trends are expected to reverse.

THE YES is part of a new wave of companies using AI to personalise how people shop online. It pulls items of clothing from brands and retailers’ websites and shows them in a feed within the app. Think of it like a clothing version of Tinder: if users like the dress being shown, they tap “yes”. If they’re not interested, they tap “no”. But, unlike Tinder, it can improve the items it shows over time by using artificial intelligence and machine learning.

Every like and dislike is fed back to the underlying machine learning models to inform each personalised feed of items users can then buy, and no two people’s recommendations are the same. “AI is simply the ability to understand consumer behaviour and act on it,” says Bornstein, the former chief operating officer of personal styling service Stitch Fix. “The problem with e-commerce is that the infrastructure doesn’t exist to do that today. You need to rebuild the tech stack.”

A swarm of drones could launch attacks on their own

By  Ameya Paleja

The system is fed with data from satellites, other reconnaissance drones, aerial vehicles, and intel collected by the ground unit.

In July 2019, unidentified drones swarmed the US Navy destroyers, triggering an alert. In May of 2021, Israel allowed the use of drone swarms to locate, identify, and attack Hamas militants, in what is likely the first-ever use of drone swarms in combat. 

Last month, we had reported that Israel deployed a semi-autonomous robot during the recent Gaza conflict. Carrying a machine gun, this robot named Jaguar, was capable of driving to a designated location, returning fire, and even self-destructing when compromised. However, the robot needed a human operator to initiate the firing from the machine gun. 

A fully autonomous drone swarm is a different level of technology altogether. It is a networked entity that is not controlled by human operators at all. Operated by artificial intelligence (AI), it can continue its mission, even if loses some drones during its mission. The machine learning system is fed with data sourced from satellites, other reconnaissance drones, and aerial vehicles, as well as intel collected by ground units. 

By Daniel Elad 

Artificial Intelligence (AI) has been creating quite a stir across all industries, including the Connected TV (CTV) realm. In fact, it has already switched from being an attribute of siloed players to become something most actors dip their toes into.

While AI-driven data mining helps build predictions and foresee peoples’ attitudes to video content, machine learning algorithms segment viewers according to their habits. With such a slew of capabilities, no wonder AI has received a warm welcome in the CTV space. 

Sage Lazzaro

If you imagine how a food and beverage company creates new offerings, your mind likely fills with images of white-coated researchers pipetting flavors and taste-testing like mad scientists. This isn’t wrong, but it’s only part of the picture today. More and more, companies in the space are tapping AI for product development and every subsequent step of the product journey.

At PepsiCo, for example, multiple teams tap AI and data analytics in their own ways to bring each product to life. It starts with using AI to collect intel on potential flavors and product categories, allowing the R&D team to glean the types of insights consumers don’t report in focus groups. It ends with using AI to analyze how those data-driven decisions played out.

“It’s that whole journey, from innovation to marketing campaign development to deciding where to put it on shelf,” Stephan Gans, chief consumer insights and analytics officer at PepsiCo, told VentureBeat. “And not just like, ‘Yeah, let’s launch this at the A&P.’ But what A&P. Where on the shelf in that particular neighborhood A&P.”