We’re half-way through the year and AI isn’t slowing down for any reason. Or anyone.
In fact, a recent survey reveals that completed, and nearly completed, AI implementations took a massive leap from 6% last year to 63% in 2022!
The tenfold increase shows just how relentless the growth and adoption across machine learning (ML), cloud migration and as-a-service offerings have been over the past year alone.
With change happening so fast and innovation becoming a constant, it can be a bit difficult to keep up with everything.
So for AI news in the month of June, we’ve got you.
Google Chrome Just Got A Whole Lot Better (With ML)
Giving users a far safer experience all across the board, the Safe Browsing function in Chrome helps protect users from malware, unwanted software and social engineering threats.
A new ML model launched by Google in March has now given Chrome the ability to detect over 2.5x more phishing attacks and malicious sites.
But wait. Google promises the AI model will offer more to improve user experience.
- It learns when users are more likely to reject notifications based on previous interactions and silences them to minimise interruptions.
- The Journeys feature tags and connects all of the pages and links that you’ve visited around a certain topic.
- Users will have real-time capabilities with Chrome that tie to their usage habits. “Maybe you like to read news articles in the morning… Or maybe voice search is more your thing… We want to make sure Chrome is meeting you where you’re at, so in the near future, we’ll be using ML to adjust the toolbar in real-time – highlighting the action that’s most useful in that moment (e.g., share link, voice search, etc.)”, says Tarun Bansal, Software Engineer for Chrome.
Future AI Robot Chefs Learn To Chew
Imagine a future where personal robot chefs can taste. Taste what? Their cooking, of course.
To make sure your dish tastes right at each stage.
Just like the ‘taste-as-you-go’ process that we use when cooking, researchers from the University of Cambridge have trained a robot to do the same. It can distinguish the saltiness of a meal at different stages of the chewing process.
Yes. A robot ‘chef’ has been trained to taste food. At different stages of the chewing process to assess whether it’s sufficiently seasoned.
But can it cook?
According to Science Daily, it can already make omelettes based on feedback given by human tasters. It was also able to taste 9 different variations of scrambled eggs and tomatoes at 3 different stages of the chewing process. Then, get this, it could produce taste maps of each dish.
“When a robot is learning how to cook, like any other cook, it needs indications of how well it did,” said Abdulali. “We want the robots to understand the concept of taste, which will make them better cooks. In our experiment, the robot can ‘see’ the difference in the food as it’s chewed, which improves its ability to taste,” says Dr Arsen Abdulali, from Cambridge’s Department of Engineering.
Faster Computing, Less Error
Shell scripts are a popular way to automate tasks on your computer.
But they can be very slow and require careful code execution.
Now, researchers from the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT have developed an innovative technique that allows you to take advantage of multiple cores to run these programs much quicker and accurately.
Their system boosts the speeds of programs that run in a UNIX shell, an environment created 50 years ago and still widely used today. Using a new process, known as parallelization, makes it possible to execute shell scripts in multiple threads at once, ensuring they finish quickly while still providing a high level of accuracy.
The method parallelizes these components. Which means it breaks them down into pieces for simultaneous execution on multiple processor cores, or CPUs.
This enables programs to execute and expedite tasks like web indexing, natural language processing (NLP), or data analysis within a much faster runtime.
“There are so many people who use these types of programs, like data scientists, biologists, engineers, and economists. Now they can automatically accelerate their programs without fear that they will get incorrect results,”
Nikos Vasilakis, research scientist in the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT.
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