What is data poisoning AI poisoning and how does it work?

What is the Google Gemini AI Model Formerly Bard?

Techniques such as GANs and variational autoencoders (VAEs) -- neural networks with a decoder and encoder -- are suitable for generating realistic human faces, synthetic data for AI training or even facsimiles of particular humans. Similar to their larger counterparts, SLMs are built on transformer model architectures and neural networks. SLM development commonly integrates techniques such as transfer learning from larger models and may incorporate advancements such as retrieval-augmented generation to optimize performance and expand the knowledge base.

For example, a summary of a complex topic is easier to read than an explanation that includes various sources supporting key points. The readability of the summary, however, comes at the expense of a user being able to vet where the information comes from. Generative AI starts with a prompt that could be in the form of a text, an image, a video, a design, musical notes, or any input that the AI system can process. Content can include essays, solutions to problems, or realistic fakes created from pictures or audio of a person.

What is unimodal vs. multimodal AI?

VLMs trained on these stories and images of people holding circular shapes connect the dots among balls, humans, dogs and the game fetch to discern a similar interpretation. They can also help interpret many other things that people often describe about images in captions to connect what might be apparent in an image within a larger context. But what’s important is that the Power Platform tools, for example, are so far the most advanced we’ve seen for enabling citizen developers to define these agents. Microsoft is still missing some pieces on that digital representation of what the people, places and things are, but it’s coming at it from a user simplicity point of view. Specifically, building these systems will involve continually improving the models about how the parts of the business work, should work and how they can work better.

Since then, various other research and commercial projects have explored how transformer models could be combined with GenAI techniques and traditional ML approaches to connect visual and language domains. The big breakthrough was the introduction of the transformer model by a team of Google researchers in 2017. This lets a new generation of algorithms simultaneously consider the relationship between multiple elements in longer sentences, paragraphs and, later on, books.

Direct vs. indirect data poisoning attacks

The landscape of generative AI is evolving rapidly, with open-source models crucial for making advanced technology accessible to all. These models allow for customization and collaboration, breaking down barriers that have limited AI development to large corporations. Image generation models create high-quality visuals or artwork from text prompts, which makes them invaluable for content creators, designers, and marketers. Whether text, images, product recommendations, or any other output, Generative AI uses natural language to interact with the user and carry out instructions. "General AI" is again an umbrella for more traditional types of artificial intelligence that have long been used for different tasks.

These breakthroughs notwithstanding, we are still in the early days of using generative AI to create readable text and photorealistic stylized graphics. Early implementations have had issues with accuracy and bias, as well as being prone to hallucinations and spitting back weird answers. Still, progress thus far indicates that the inherent capabilities of this generative AI could fundamentally change enterprise technology how businesses operate. Going forward, this technology could help write code, design new drugs, develop products, redesign business processes and transform supply chains. Many generative AI tools can only process one type of data and deliver outputs in that same modality. With the introduction of multimodal AI, generative AI tools can now process various data types and deliver a range of outputs that don’t have to match the input.

Examples and uses of autonomous agents

SLMs are ideal for specialized, resource-constrained applications, offering cost-effective and rapid deployment capabilities. In contrast, LLMs are well suited for complex tasks that require deep contextual understanding and broad generalization capabilities, typically at a higher cost with more resource requirements. Since they are working with multiple different modalities, multimodal models require a lot of data to function properly. For example, if a model aims to convert text to images and vice versa, then it needs to have a robust set of both text and image data. Self-driving cars process and interpret data from multiple sources, thanks to multimodal AI. Cameras provide visual information about the vehicle’s environment, radar detects objects and their speed whileLiDAR measures the distances between them, and GPS provides location and navigation data.

Though the broad goal of human-like intelligence is fairly straightforward, the details are nuanced and subjective. The pursuit of AGI therefore comprises the development of both a framework to understand intelligence in machines and the models able to satisfy that framework. That deep understanding, sometimes called parameterized knowledge, makes LLMs useful in responding to general prompts at light speed. However, it does not serve users who want a deeper dive into a current or more specific topic.

Google plans to expand Gemini's language understanding capabilities and make it ubiquitous. However, there are important factors to consider, such as bans on LLM-generated content or ongoing regulatory efforts in various countries that could limit or prevent future use of Gemini. One concern about Gemini revolves around its potential to present biased or false information to users. For example, as is the case with all advanced AI software, training data that excludes certain groups within a given population will lead to skewed outputs. At launch on Dec. 6, 2023, Google said Gemini would comprise a series of different model sizes, each designed for a specific set of use cases and deployment environments. As of Dec. 13, 2023, Google enabled access to Gemini Pro in Google Cloud Vertex AI and Google AI Studio.

It's important to note that most models listed here, even those with traditionally open-source licenses like Apache 2.0 or MIT, do not meet the Open Source AI Definition (OSAID). This gap is primarily due to restrictions around training data transparency and usage limitations, which OSAID emphasizes as essential for true open-source AI. However, certain models, such as Bloom and Falcon, show potential for compliance with minor adjustments to their licenses or transparency protocols and may achieve full compliance over time. The Open Source Initiative (OSI) recently introduced the Open Source AI Definition (OSAID) to clarify what qualifies as genuinely open-source AI. To meet OSAID standards, a model must be fully transparent in its design and training data, enabling users to recreate, adapt, and use it freely. The Google Gemini models are used in many different ways, including text, image, audio and video understanding.

• With the introduction of multimodal AI, generative AI tools can now process various data types and deliver a range of outputs that don’t have to match the input.
• The applications for this technology are growing every day, and we’re just starting to explore the possibilities.
• In contrast, generative AI is designed to generate novel content based on user input and the unstructured data on which it's trained.

A small language model (SLM) is a generative AI technology similar to a large language model (LLM) but with a significantly reduced size. GPT-4o and GPT-4, two models that power ChatGPT, are multimodal — so yes, ChatGPT is capable of being multimodal. Google’s Gemini is an example of a unified model running on a single architecture, and this list is likely to grow as more companies build their own unified models. While major tech players like Meta, Google and OpenAI are still experimenting with this technology, it’s only a matter of time before it enters the mainstream as it undergoes improvements. “Your robot — whether that’s a drone or a car or humanoid — will take some kind of action in the physical world that will have physical consequences,” Englot said. “If you don’t have any guardrails on a model that’s controlling a robot, it’s possible hallucinations or incorrect interpretations of the data could lead to the robot taking actions that could be dangerous or harmful.

Audio models process and generate audio data, enabling speech recognition, text-to-speech synthesis, music composition, and audio enhancement. Vision models analyze images and videos, supporting object detection, segmentation, and visual generation from text prompts. This setup establishes a robust framework for efficiently managing Gen AI models, from experimentation to production-ready deployment. Each tool set possesses unique strengths, enabling developers to tailor their environments for specific project needs. The OSAID has sparked notable dissent among prominent open-source community members.

This will help your model minimize output bias, better understand its tasks and yield more effective outputs. AI hallucinations are similar to how humans sometimes see figures in the clouds or faces on the moon. In the case of AI, these misinterpretations occur due to various factors, including overfitting, training data bias/inaccuracy and high model complexity. LAMs employ sophisticated algorithms that use a combination of neural networks and symbolic AI techniques for decision-making. Large action models use neuro-symbolic AI, combining pattern recognition with logical reasoning to determine the best action. Developers should consider adopting technology that automates this process in order to operate at scale.

Besides Google Gemini, other well-known examples of multimodal AI include OpenAI’s DALL-E and GPT-4o, Meta’s ImageBind and Anthropic’s Claude 3 model family. Multimodal AI refers to an artificial intelligent system that uses multiple types of data (including text, images, video and audio) to generate content, form insights and make predictions. Most recently, human supervision is shaping generative models by aligning their behavior with ours.

Why Meta’s ‘open source’ AI isn’t all it seems - Euronews

Why Meta’s ‘open source’ AI isn’t all it seems.

Posted: Mon, 28 Oct 2024 07:00:00 GMT [source]

Neuro-symbolic AI combines neural networks with rules-based symbolic processing techniques to improve artificial intelligence systems' accuracy, explainability and precision. The neural aspect involves the statistical deep learning techniques used in many types of machine learning. The symbolic aspect points to the rules-based reasoning approach that's commonly used in logic, mathematics and programming languages. Organizations can create foundation models as a base for the AI systems to perform multiple tasks.

However, there are age limits in place to comply with laws and regulations that exist to govern AI. At its release, Gemini was the most advanced set of LLMs at Google, powering Bard before Bard's renaming and superseding the company's Pathways Language Model (Palm 2). As was the case with Palm 2, Gemini was integrated into multiple Google technologies to provide generative AI capabilities. Although the term embodied AI or embodied intelligence is relatively new, it's related to mechanisms like adaptive control systems, cybernetics and autonomous systems, which have been around for centuries.

For example, a newer Attribution, Relation and Order benchmark measures visual reasoning skills better than traditional metrics developed for machine translation. More work is also required to develop better metrics for various use cases in medicine, industrial automation, warehouse management and robotics. For example, when we see a car with a dent sitting in the middle of the road and an ambulance nearby, we instantly know that a crash probably occurred even though we didn't see it.