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Most Read: How AI Deepfakes Threaten Cybersecurity; Waymo Launches AI Model for Autonomous Driving
Also inside, Amazon invests $110M in AI research of Trainium chips, Ericsson invests $456M for AI, tech research in Canada and more
3 Min Read
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Here are the most-read stories on AI Business this week.
How AI Deepfakes Threaten Cybersecurity
When most people think of AI-generated deepfakes, they probably think of videos of politicians or celebrities being manipulated to make it appear as though they said or did something they didn’t. These can be humorous or malicious. When deepfakes are in the news, for instance, it is usually in connection with a political misinformation campaign.
What many people don’t realize, however, is that the malicious use of deepfakes extends well beyond the political realm. Scammers are increasingly adept at using real-time deepfakes to impersonate individuals with certain permissions or clearances, thus granting them access to private documents, sensitive personal data and customer information. This is a serious cybersecurity threat for businesses and one that not enough businesses are protected against.
Waymo Launches AI Model for Autonomous Driving
Waymo has launched a new AI research model for autonomous driving.
The End-to-End Multimodal Model for Autonomous Driving (EMMA) was specifically trained and fine-tuned for autonomous driving, leveraging Gemini's world knowledge to better understand complex road scenarios.
Waymo released a research paper on its new model, which the company said demonstrates how multimodal models can be applied to autonomous driving, while also exploring the pros and cons of the pure end-to-end approach. “
“Building on top of Gemini and leveraging its capabilities, we created a model tailored for autonomous driving tasks such as motion planning and 3D object detection,” Waymo stated in the announcement.
Amazon Invests $110M in AI Research of Trainium Chips
Amazon is making a major investment in AI research as the company looks to reduce its reliance on Nvidia and develop its own in-house chips.
The AWS Trainium is a custom-built machine learning (ML) chip designed for deep learning training and inference tasks.
The investment would support generative AI research at universities using Trainium chips. The program, dubbed Build on Trainium, would provide researchers the ability to develop new AI architectures, machine learning libraries and performance enhancements for large-scale distributed AWS Trainium UltraClusters, groups of AI accelerators working in unison on complex computational tasks, Amazon said.
See inside the lab where AWS makes custom chips
Ericsson Invests $456M for AI, Tech Research in Canada
Ericsson and the government of Canada have expanded their partnership with a substantial funding increase, with Ericsson committing $456 million to research facilities in Ottawa and Montreal.
This investment aims to accelerate advancements in AI, quantum technologies, 5G Advanced, 6G, Cloud RAN and network API technologies while creating and upskilling jobs and internships.
“Our partnership with Ericsson solidifies Canada’s position as a leader in next-generation networks,” said Canada’s minister of innovation, science and industry François-Philippe Champagne.
“With the increased investment, we’ll not only support the 5G networks of today, but also advance the technologies that will shape our future and continue to make Canada a leader in these areas.”
How to Use AI for an Ethical and Sustainable Future
The potential of AI is exciting, with many organizations rushing to embrace this next-generation technology, which can and should be used to generate value for all society. AI can provide equitable outcomes for all, helping to build a fully inclusive society. A clear example of this is how AI can support those who have accessibility requirements. There are, however, downsides for people and the planet which are often overlooked.
To bring true value to society and the environment, it is important to look at the overall impact of AI. We should aim to maximize AI’s positive effects while minimizing any negative impact. This means making AI ethical and sustainable, reducing its harmful impacts and enhancing its benefits.
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