Lifeboat Foundation

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At their best, AI systems extend and augment the work we do, helping us to realize our goals. At their worst, they undermine them. We’ve all heard of high-profile instances of AI bias, like Amazon’s machine learning (ML) recruitment engine that discriminated against women or the racist results from Google Vision. These cases don’t just harm individuals; they work against their creators’ original intentions. Quite rightly, these examples attracted public outcry and, as a result, shaped perceptions of AI bias into something that is categorically bad and that we need to eliminate.

While most people agree on the need to build high-trust, fair AI systems, taking all bias out of AI is unrealistic. In fact, as the new wave of ML models go beyond the deterministic, they’re actively being designed with some level of subjectivity built in. Today’s most sophisticated systems are synthesizing inputs, contextualizing content and interpreting results. Rather than trying to eliminate bias entirely, organizations should seek to understand and measure subjectivity better.

Artificial Intelligence is the buzzword of the year with many big giants in almost every industry trying to explore this cutting-edge technology. Right from self-checkout cash registers to AI-based applications to analyse large data in real-time to advanced security check-ins at the airport, AI is just about everywhere.

Currently, the logistics industry is bloated with a number of challenges related to cost, efficiency, security, bureaucracy, and reliability. So, according to the experts, new age technologies like AI, machine learning, the blockchain, and big data are the only fix for the logistics sector which can improve the supply chain ecosystem right from purchase to internal exchanges like storage, auditing, and delivery.

AI is an underlying technology which can enhance the supplier selection, boost supplier relationship management, and more. When combined with big data analytics AI also helps in analysing the supplier related data such as on-time delivery performance, credit scoring, audits, evaluations etc. This helps in making valuable decisions based on actionable real-time insights.

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Over the last half-decade, quantum computing has attracted tremendous media attention. Why?

After all, we have computers already, which have been around since the 1940s. Is the interest because of the use cases? Better AI? Faster and more accurate pricing for financial services firms and hedge funds? Better medicines once quantum computers get a thousand times bigger?

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The metaverse is becoming one of the hottest topics not only in technology but in the social and economic spheres. Tech giants and startups alike are already working on creating services for this new digital reality.

The metaverse is slowly evolving into a mainstream virtual world where you can work, learn, shop, be entertained and interact with others in ways never before possible. Gartner recently listed the metaverse as one of the top strategic technology trends for 2023, and predicts that by 2026, 25% of the population will spend at least one hour a day there for work, shopping, education, social activities and/or entertainment. That means organizations that use the metaverse effectively will be able to engage with both human and machine customers and create new revenue streams and markets.

While it is still unclear how cryptocurrencies will alter in value and where we will be in 2023 — one thing is clear, they are undoubtedly here to stay.

As 2022 draws to a close, the cryptocurrency market is under great scrutiny. From the crypto winter of 2021 that saw Bitcoin lose almost a third of its value and other cryptocurrencies follow suit to security issues with crypto exchanges, bridges, and web 3.0 apps, and of course, FTX’s dramatic failure, it’s almost impossible to disbelieve tales of gloom. However, looking at the forecasts for 2023, it appears the market may rebound, and now may even be an ideal moment to invest.

Continue reading “Why blockchain will remain a big deal in 2023 and beyond” »

A recent study finds that software engineers who use code-generating AI systems are more likely to cause security vulnerabilities in the apps they develop. The paper, co-authored by a team of researchers affiliated with Stanford, highlights the potential pitfalls of code-generating systems as vendors like GitHub start marketing them in earnest.

“Code-generating systems are currently not a replacement for human developers,” Neil Perry, a PhD candidate at Stanford and the lead co-author on the study, told TechCrunch in an email interview. “Developers using them to complete tasks outside of their own areas of expertise should be concerned, and those using them to speed up tasks that they are already skilled at should carefully double-check the outputs and the context that they are used in in the overall project.”

The Stanford study looked specifically at Codex, the AI code-generating system developed by San Francisco-based research lab OpenAI. (Codex powers Copilot.) The researchers recruited 47 developers — ranging from undergraduate students to industry professionals with decades of programming experience — to use Codex to complete security-related problems across programming languages including Python, JavaScript and C.

One afternoon in the fall of 2019, in a grand old office building near the Arc de Triomphe, I was buzzed through an unmarked door into a showroom for the future of surveillance. The space on the other side was dark and sleek, with a look somewhere between an Apple Store and a doomsday bunker. Along one wall, a grid of electronic devices glinted in the moody downlighting—automated license plate readers, Wi-Fi-enabled locks, boxy data processing units. I was here to meet Giovanni Gaccione, who runs the public safety division of a security technology company called Genetec. Headquartered in Montreal, the firm operates four of these “Experience Centers” around the world, where it peddles intelligence products to government officials. Genetec’s main sell here was software, and Gaccione had agreed to show me how it worked.

He led me first to a large monitor running a demo version of Citigraf, his division’s flagship product. The screen displayed a map of the East Side of Chicago. Around the edges were thumbnail-size video streams from neighborhood CCTV cameras. In one feed, a woman appeared to be unloading luggage from a car to the sidewalk. An alert popped up above her head: “ILLEGAL PARKING.” The map itself was scattered with color-coded icons—a house on fire, a gun, a pair of wrestling stick figures—each of which, Gaccione explained, corresponded to an unfolding emergency. He selected the stick figures, which denoted an assault, and a readout appeared onscreen with a few scant details drawn from the 911 dispatch center. At the bottom was a button marked “INVESTIGATE,” just begging to be clicked.

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It’s as good a time as any to discuss the implications of advances in artificial intelligence (AI). 2022 saw interesting progress in deep learning, especially in generative models. However, as the capabilities of deep learning models increase, so does the confusion surrounding them.

Continue reading “What we learned about AI and deep learning in 2022” »

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Tomorrow morning, I head south. Straight down I-95, from central New Jersey to northeast Florida, where I will be setting up my laptop in St. Augustine for the next two months. It’s about as far from Silicon Valley as I can be in the continental U.S., but that’s where you’ll find me gearing up for the first artificial intelligence (AI) news of 2023.

These are the 5 biggest AI stories I’m waiting for: