Comprehensive look at AI’s role in 2024’s technological evolution

In 2024, Artificial Intelligence (AI) is changing things a lot. It is bringing new and exciting trends that will affect our lives in big ways. Let us take a closer look at what is happening with AI this year.

1. Tailored Chatbots

In 2024, the buzz around AI is not just for tech enthusiasts, but it is becoming a tool for everyone. Tech giants like Google and OpenAI are investing in generative AI with an aim to prove that AI products can be not only powerful but also profitable. The focus is on customized chatbots – user-friendly platforms that empower individuals to create their mini chatbots without any coding skills. This is a big change that makes AI available to everyone.

1.1 Rise of User-Friendly Platforms

Both Google and OpenAI have introduced web-based tools that allow anyone to become a generative AI app developer. These platforms enable users to harness the power of language models like GPT-4 and Gemini without diving into the complexities of coding. This is a big deal because now anyone can play around with AI and make it work for what they need.

1.2 Real-World Applications

The real value emerges as generative AI becomes practical for non-tech individuals. State-of-the-art models like GPT-4 and Gemini go beyond processing text; they can handle images and videos. Imagine a real estate agent effortlessly generating property descriptions by uploading previous listings, videos and photos. This marks a significant leap, showcasing the potential of AI in various real-world applications.

1.3 Challenges on the Horizon

However, the success of this democratization of AI depends on overcoming some critical challenges. Sometimes, the words computers use might not be right, and they can show some unfair preferences. Also, letting these computer programs explore the internet could be a problem for security. Tech companies need to work on fixing these things to make sure their AI is reliable and trustworthy.

2. Generative AI in Video

While generative models producing photorealistic images became commonplace in 2022, the frontier in 2024 is text-to-video. Now, instead of just turning words into pictures, they are turning words into videos. This brings new exciting things and some challenges for generative AI.

2.1 Evolution of Generative Models

Platforms like OpenAI’s DALL-E, Stability AI’s Stable Diffusion and Adobe’s Firefly have set the stage for impressive generative capabilities. However, the next horizon is text-to-video, taking everything learned from text-to-image and supersizing it. A year ago, early attempts at generating video clips from multiple still images were shaky, but rapid technological improvements are changing the game.

2.2 Runway’s Stride in Generative Video Models

Startups like Runway, releasing new versions of its tools regularly, have made significant strides in generative video models. Their newest model, Gen-2, makes short videos that look really good, almost as good as what big movie companies like Pixar make. Runway even has a film festival showing movies made with AI, showing that AI is becoming a big deal in creative stuff.

2.3 Impact on Movie Production and Beyond

Major movie studios, including Paramount and Disney, are integrating generative AI into their production pipelines. This cool tech is changing how movies look by making awesome special effects and doing things we didn’t think were possible before. The film industry has witnessed deepfake technology in action, as seen in Indiana Jones and the Dial of Destiny, featuring a de-aged deepfake of Harrison Ford. Besides making things fun, deepfake tech is getting popular for ads and training, but it is also making people think about what’s right and wrong in filmmaking.

3. AI-Generated Election Disinformation

With the increasing use of AI in political contexts, concerns about AI-generated election disinformation and deepfakes are on the rise. In recent elections everywhere, politicians are using computer tools to change what people think and say bad things about their opponents.

3.1 Politicization of AI in Elections

In Argentina, presidential candidates utilized AI-generated images and videos to tarnish their opponents’ images. Similarly, Slovakia experienced the spread of deepfakes targeting a liberal pro-European party leader during elections. In the U.S., Donald Trump liked a group that used computer tools to make memes with mean and unfair ideas, showing that even technology can be part of politics.

3.2 Challenges in Combatting Disinformation

The proliferation of AI-generated election disinformation poses significant challenges. The difficulty in distinguishing real content from AI-generated content online is growing, amplifying the potential impact on political outcomes. The inflamed and polarized political climate coupled with sophisticated AI manipulation techniques raises concerns about the severe consequences of misinformation in elections.

Also Read: Non-invasive data governance for modern organizations

3.3 Real-Time Experiment in Disinformation Combat

As more fake news made by computers spreads, the next year will be a really important time for stopping false information. Tracking and mitigating AI-generated content is still in the early stages of development. Watermarks, such as Google DeepMind’s SynthID, are being explored, but their effectiveness remains voluntary and not foolproof. Social media platforms continue to struggle in promptly removing misinformation. Brace yourself for a real-time experiment in countering AI-generated fake news.

4. Multitasking Robots

Because generative AI works well, scientists who make robots are trying to create robots that can do lots of different things, not just one job. The shift from using multiple small models for specific tasks to employing single, monolithic models that can multitask is gaining momentum.

4.1 Evolution of Multimodal Models

In recent years, AI has witnessed a shift from small, specialized models to multimodal models like GPT-4 and Google DeepMind’s Gemini. These models can do both talking and seeing jobs, making it easier to solve problems in one go.

4.2 Promise of General-Purpose Robots

The idea of general-purpose robots that can multitask is gaining traction. Instead of teaching different robots different things, one model can help robots do lots of jobs. Projects like DeepMind’s Robocat and RT-X are making robots learn to do more things with one training.

4.3 Challenges in Data Availability

Despite the promise, the lack of data remains a significant challenge in developing general-purpose robots. Unlike generative AI, which draws on vast datasets of text and images, robots have limited sources of high-quality data for learning tasks such as industrial or domestic chores.

Also Read: The chip-driven future of fully homomorphic encryption

4.4 Learning by Trial and Error

Researchers are exploring innovative approaches to address the data scarcity issue. Techniques that enable robots to learn by trial and error, generating their training data as they go, are showing promise. People like Lerrel Pinto at New York University are leading the way in helping robots learn and get better by trying things out.

4.5 Real-World Applications

The application of this approach is evident in driverless cars. Startups like Wayve, Waabo and Ghost are pioneering a new wave of self-driving AI that employs a single large model to control a vehicle, a departure from using multiple smaller models for specific driving tasks. This shift has enabled smaller companies to compete with industry giants like Cruise and Waymo, with Wayve even testing its driverless cars on the bustling streets of London.