Google unleashes a new AI called Gemini

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Your gateway to discovering the world of Artificial Intelligence!

A New York man won his second $10 million lottery prize in two years this week. Last night I got the daily Wordle in only two attempts…so who's the real lucky one now, huh?

A sneak peek of what’s below:

• How do Recommender Systems work?

• Google has Twins? Release of Gemini

• How Stem Solutions is looking to use AI to change energy consumption

• Get creative by imagining winter wonderlands!

How do recommendation systems work?

Turning AI topics into a walk in the park, not a mind-boggling maze

Recommendations = Personalization

Holiday season is here and you're likely engaged in one of these two things: browsing Amazon for gifts or logging onto Netflix to watch a holiday-themed movie.

Both of those companies are pioneers in using "Recommendation Systems" to keep you engaged and it's a large part of why they have become so successful. Let's breakdown what these systems are and why they have become essential.

Recommendation systems, also referred to as "engines", represent a category of computer algorithms designed for the purpose of enhancing the user experience of a service. They consider user preferences, behaviors and interactions to provide tailored recommendations to you.

It takes mountains of data for a recommendation engine to be so successful. These engines aren't just taking your data into account, but all of the users on that site or service. By grouping users with similar characteristics, the engine creates patterns and predictions, delivering a highly personalized experience to everyone.

While the exact working of their engines are proprietary, here are two fundamental forms of recommendation systems that contribute to the hybrid approach employed by both Amazon and Netflix:

Content-Based Filtering: Looking at the attributes and features of products that a user has previously shown interest in. For example, if you frequently browse science fiction books, Amazon will recommend other science fiction books based on the shared characteristics of the books you've viewed or purchased.

Collaborative Filtering: Analyzing the behavior of millions of users and identifies patterns. If users who bought similar items also bought other items, the system suggests those related products to customers. They also use user-to-user collaborative filtering, where recommendations are made based on what similar users have liked or purchased.

Let's take a look at a classic pros and cons list of how recommendation engines impact users:

Pros

• Personalized Recommendations: Users receive tailored suggestions that match their preferences, making it easier to find content or products they like.

• Enhanced Satisfaction: By receiving recommendations aligned with their tastes, users are more likely to be satisfied with their choices.

• Convenience: Users benefit from the convenience of finding relevant items quickly, with low effort, simplifying decision-making.

Cons

• Filter Bubble: Users may become trapped in a "filter bubble" where they are exposed only to content or products similar to their previous choices, potentially limiting diversity.

• Cold Start: For newcomers, receiving relevant recommendations can be challenging because recommender systems rely on historical user data and preferences to make personalized suggestions.

• Privacy Concerns / Lack of Control: Users may worry about the level of data collection and profiling required to provide personalized recommendations, raising privacy concerns and making them feel they have limited control over their online experience when algorithms heavily influence their choices.

AI News Flash

Catch the latest AI news that is making all of the headlines this week!

Google Gemini

This week, Google unveiled its latest AI breakthrough named "Gemini." The name pays homage to two of Google's prominent AI labs and draws inspiration from NASA's Gemini project, which paved the way for the historic Apollo moon landing.

Gemini is initially making its debut in their chatbot, "Bard," but it will soon be integrated into messaging services on Pixel smartphones, as well as finding its way into Google Search and Chrome in the coming months. Google has teased a much stronger version of Gemini expected in 2024.

This is one of those major milestone releases that will be make it or break it for Google - it's definitely on par with the significance of their PageRank algorithm, which catapulted the search engine into the global spotlight in 1998 and changed the way we use the internet today. Not many other nouns have turned into a verb like "google it" - will we be saying "Gemini it" in the future?

Why does this matter?

Gemini Pro, the model introduced this week surpassed GPT-3.5 (ChatGPT), in 6/8 widely used AI benchmarks. Google's unveiling of Gemini isn't just a technological feat; it's a significant development in the ever-competitive AI landscape. With Gemini Pro, outperforming its predecessors and rivals on multiple benchmarks, Google has stepped up its game to stay ahead in the AI race.

The emergence of this powerful AI model intensifies the competition between Google and other tech giants like Microsoft and Amazon, who are also investing heavily in AI advancements. As these giants continue to push the boundaries of AI capabilities, it's not only about staying at the forefront of innovation but also about maintaining a competitive edge in delivering smarter and safer AI-powered solutions to users around the world. It also poses more questions around big tech "owning" AI and the concentration of power.

So Microsoft (and OpenAI), Amazon, and Google have now released their AI warriors. Who's next? Apple?

Other Stories We're Following:

• A Brazilian city became the first globally to pass a piece of legislation fully written by AI…unknowingly

• Meta (Facebook) and IBM are forming an alliance to make AI more open-source

Get Creative with AI

Practice using AI and unleash creativity with your new collaborator

Copy the prompts below and paste them into ChatGPT, BingAI, Google Bard, or your generative AI tool of choice and let AI work its magic. Share your results with us…and take it a step beyond the initial prompts and see what you can create!

Text-based

• Describe your ideal winter wonderland escape. What does it look like? Are you building snowmen, sipping cocoa by the fire, embarking on a snowy adventure, or perhaps cozying up with a captivating book?

Visual-based*

• Picture a snowy mountain landscape alive with skiers and snowboarders gliding down pristine slopes under a bright blue winter sky. Ski lifts crisscross the scene, connecting a series of scenic trails, while a cozy ski village bustles at the mountain's base. Majestic mountain peaks rise in the background, framing this winter sports paradise.

Visit us on Instagram and Twitter to see how our examples turned out!

*Visual-based examples are for users of Dall-E, BingAI, or premium users of ChatGPT - may require paid subscription.