New Cyber Threats Expected from AI in 2024

PLUS: New AI Embeddings Shrink Footprint Without Sacrificing Performance.

Welcome back! Today is Tuesday, January 2nd and Square Enix announces an aggressive AI strategy for 2024 while cybersecurity expert Mikko Hyppönen warns of AI-powered deepfakes and scams. New to The Intelligence Age? Sign up here.
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Square Enix's Aggressive AI Strategy for 2024

Square Enix has announced an aggressive AI strategy for 2024, aiming to significantly integrate AI and other advanced technologies into their content development and publishing processes. President Takashi Kiryu's vision includes enhancing development productivity and marketing sophistication in the short term. Long-term goals involve leveraging AI to create new forms of content, recognizing technological innovation as a key driver for business opportunities. The company's history of adopting emerging technologies, such as CGI in "Final Fantasy: The Spirits Within," and AI in "Final Fantasy 7 Remake," underscores their commitment to exploring and utilizing cutting-edge technologies in their operations.

This strategic move comes as Square Enix views 2024 as a starting point for further growth, aligning with their new corporate strategy, the "Grand Design." The plan reflects a broader industry trend where gaming companies are increasingly focusing on digital platforms and AI-driven solutions. Square Enix’s previous ventures into blockchain entertainment and Web 3.0 indicate their willingness to embrace and invest in controversial and emerging technologies, despite potential challenges and public apprehension. This strategy highlights Square Enix's ambition to stay at the forefront of technological innovation in the gaming industry. PushSquare

Cybersecurity Guru Mikko Hyppönen’s 5 Most Fearsome AI Threats For 2024

Mikko Hyppönen, a cybersecurity sage with a storied career in malware combat, has flagged five AI threats that could dominate 2024. Deepfakes, which have seen a 3000% surge in fraudulent use, are a particular concern. Hyppönen predicts that these digitally altered deceptions, already complicating information warfare and online scams, are on the brink of a troubling proliferation. His prescription? A retroactive measure: safe words in digital communications as a bulwark against deception.

Another looming challenge is the rise of deep scams, magnified by automation's efficiency. Imagine the scale of deceit if a con artist's toolkit included advanced AI for crafting messages or generating fake images—Hyppönen envisions a scenario where thousands could be targeted simultaneously. The implications are stark: as generative AI propels the capabilities of scammers, the traditional safeguards become obsolete, demanding new strategies in digital trust and verification. thenextweb

Why does this matter?

As we brace for AI's potential misuse in scams and malware, consider its role in autonomous decision-making systems. What happens when AI, designed to streamline corporate or governmental decisions, starts reflecting the biases and strategic imperatives of adversarial entities? This silent infiltration could shift power balances in subtle yet profound ways.

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Research Review

Memory-Efficient Embeddings

In the realm of AI, where data is king, the challenge of handling categorical variables in large-scale systems is a daunting task. Dr. Robert Köbler points to the limitations of one-hot encoding, especially when faced with a massive number of categories. The innovative solution? A new breed of embedding layers. These compact, dense vectors are revolutionizing the way we approach memory management in natural language processing and recommendation systems.

Köbler's exploration into memory-efficient embeddings reveals a path to shrinking the resource footprint of our AI models. By adjusting the embedding dimension, a critical hyperparameter, significant reductions in memory usage are achieved without sacrificing performance. Real-world application in rating prediction tasks showcases the practicality of these methods, offering a glimpse into the future of leaner, more efficient AI architectures. towardsdatascience

Written by Isaac R. Ward, Casey Clifton, and Alex Brogan.

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