Use of amorphous organic polymer that conducts electricity. making an organic polymer that retains its conductive properties without needing to have an ordered structure so it can self-heal. Self-grow and act as neurons in the human brain for use with Holistic AI.
made with tetrathiafulvalene (TTF). The molecules is made from conjugated rings of sulphur and carbon which allow electrons to delocalize across the structure, making TTF a “voracious π-stacker,”
Use of BEDT-TTF, BEST (=bis(ethylenediseleno)tetrathiafulvalene), and BETS salts (Scheme 1) of a simple organic anion, isethionate (HOC2H4SO3−) to develop future Holistic AI (HAI) systems that can learn like the human brain creating neural networks and ability to self-learn and selfheal.

The future of holistic AI (HAI) is to learn how to accurately interpret content more holistically. This means working in multiple modalities (such as text, speech, and images) at once. For example, recognizing whether a meme is hateful requires consideration.
both the image, and content of the meme will need to be considered by the AI. This will require building multimodal models for AI with augmented and virtual reality devices, so they can recognize the sound of an alarm, for example, and display an alert showing which direction the sound is coming from.
Historically, analysing such different formats of data together — text, images, speech waveforms, and video, each with a distinct architecture — has been extremely challenging for machines.
Over the last couple of years, organisations researching the future of holistic AI (HAI) have produced a slew of research projects, each addressing an important challenge of multimodal perception — from solving a shortage of publicly available data for training, for example, Hateful Memes , to a creating single algorithm for vision, speech, and text, to building foundational models that work across many tasks, to finding the right model parameters.
Today, X-HAL is sharing a summary of some of the research being conducted.

Omnivore: A single model for images, videos, and 3D data

New Omnivore models being developed can operate on image, video, and 3D data using the same parameters — without degrading performance on modality-specific tasks. For example, it can recognize 3D models of some basic objects and some simple videos. This enables radically new capabilities, such as AI systems that can search and detect content in both images and videos. Omnivore has achieved state-of-the-art results on popular recognition tasks from all three modalities, with particularly strong performance on video recognition. This could have a major impact on defense systems, drone videos, and the data and Intelligence of military command and control systems. This includes C2, C4I and CSRC. It’s probably the largest expanding market for Holistic AI (HAI).

FLAVA: A foundational model spanning dozens of multimodal tasks

FLAVA represents a new class of “foundational model” that’s jointly trained to do over 35 tasks across domains, including image recognition, text recognition, and joint text-image tasks. For instance, the FLAVA model can single-handedly describe the content of an image, reason about its text entailment, and answer questions about the image. FLAVA also leads to impressive zero-shot text and image understanding abilities over a range of tasks, such as image classification, image retrieval, and text retrieval.

FLAVA not only improves over prior work that is typically only good at one task but, unlike prior work, it also uses a shared trunk that was pre-trained on openly available public pairs — which will help further advance research.  Like Omnivore it promises to have a large impact on defence and future warfare providing better analyzed and more details information from drone reconnaissance videos and advanced information for central command and control to make more informative decisions.

CM3: Generalizing to new multimodal tasks

CM3 is one of the most general open-source multimodal models available today. By training on a large corpus of structured multimodal documents, it can generate completely new images and captions for those images. It can also be used in our setting to infill complete images or larger structured text sections, conditioned on the rest of the document. Using prompts generated in an HTML-like syntax, the exact same CM3 model can generate new images or text, caption images, and disambiguate entities in text.

Traditional approaches to pretraining have focused on mixing the architectural choices (e.g., encoder-decoder) with objective choices (e.g., masking). Our novel approach of “causally masked objective” gets the best of both worlds by introducing a hybrid of causal and masked language models.

Data2vec: The first self-supervised model that achieves SOTA for speech, vision, and text

Research in self-supervised learning today is almost always focused on one modality. In recent breakthrough research data2vec research, we show that the exact same model architecture and self-supervised training procedure can be used to develop state-of-the-art models for recognition of images, speech, and text. Data2vec can be used to train models for speech or natural languages. Data2vec demonstrates that the same self-supervised algorithm can work well in different modalities — and it often outperforms the best existing algorithms.

What’s next for holistic AI (HAI) and multimodal understanding?

Data2vec models are currently trained separately for each of the various modalities. But X-HAL research results from Omnivore, FLAVA, and CM3 suggest that, over the horizon, we may be able to train a single AI model that solves challenging tasks across all the modalities. Such a multimodal model would unlock many new opportunities. For example, it would further enhance our ability to comprehensively understand the content of social media posts in order to recognize hate speech or other harmful content. It could also help us build AR glasses that have a more comprehensive understanding of the world around them, unlocking exciting new applications in the metaverse. The driving factors are likely to be military and defense providing advanced capabilities to support drones, soldier-less warfare, and enhanced central control and command decision making.

As interest in multimodality has grown, at X-HAL holistic AI (HAI) consultants we want researchers to have great tools for quickly building and experimenting with multimodal, multitask models at scale.

You can visit our websites to stay up to date and see the latest papers and blogs we post. Infoai.uk and Kuldeepuk-kohli.com.

Thanks

What is Holistic AI?
Holistic AI refers to an approach in artificial intelligence that aims to create systems that can understand and interact with humans in a more comprehensive and human-like manner. It involves integrating multiple AI technologies and capabilities, such as natural language processing, machine learning, computer vision, and reasoning, to create a more holistic and intelligent AI system. The goal of holistic AI is to develop AI systems that can understand and respond to human needs and context, rather than just performing specific tasks in isolation. It aims to create AI systems that can understand and interpret human language, emotions, and intentions, and provide more personalized and context-aware responses. Holistic AI also takes into consideration ethical and social aspects, such as fairness, transparency, and accountability, in the design and development of technology.
How does Holistic AI work?
Holistic AI works by integrating multiple AI technologies and capabilities to create a more comprehensive and intelligent system. Here are the key components and processes involved in holistic AI: 1. Natural Language Processing (NLP): NLP enables the AI system to understand and interpret human language, including speech and text. It involves tasks such as language understanding, sentiment analysis, and language generation. 2. Machine Learning (ML): ML algorithms allow the AI system to learn from data and improve its performance over time. It involves training the system on large datasets to recognize patterns, make predictions, and make decisions. 3. Computer Vision: Computer vision enables the AI system to understand and interpret visual information, such as images and videos. It involves tasks such as object recognition, image recognition, and natural language understanding.
What is the difference between Holistic AI and AI?
The main difference between Holistic AI and AI lies in their scope and approach. AI (Artificial Intelligence) refers to the broader field of developing computer systems that can perform tasks that typically require human intelligence, such as speech recognition, problem-solving, and decision-making. AI focuses on creating intelligent systems that can perform specific tasks efficiently and accurately. On the other hand, Holistic AI takes a more comprehensive and integrated approach. It aims to create AI systems that can understand and interact with humans in a more human-like manner. Holistic AI integrates multiple AI technologies and capabilities, such as natural language processing, machine learning, computer vision, and reasoning, to create a more holistic and intelligent system. It focuses on creating AI systems that can understand and respond to human emotions.
What is Machine learning?
Machine learning is a subfield of artificial intelligence, which is broadly defined as the capability of a machine to imitate intelligent human behavior. Artificial intelligence systems are used to perform complex tasks in a way that is like how humans solve problems.
The goal of AI is to create computer models that exhibit “intelligent behaviors” like humans, according to Boris Katz, a principal research scientist and head of the InfoLab Group at CSAIL. This means machines that can recognize a visual scene, understand a text written in natural language, or perform an action in the physical world.

Machine learning is one way to use AI. It was defined in the 1950s by AI pioneer Arthur Samuel as “the field of study that gives computers the ability to learn without explicitly being programmed.”
Does Holistic AI still rely on background computer hardware like silicon chips, memory, etc?
The short answer is yes. The technology required is far more advanced. Specialized computer hardware is often used to execute artificial intelligence (AI) programs faster and with less energy, such as Lisp machines, neuromorphic engineering, event cameras, and physical neural networks
AI workloads are massive, demanding a significant amount of bandwidth and processing power. As a result, AI chips require a unique architecture consisting of the optimal processors, memory arrays, security, and real-time data connectivity.
Modern artificial intelligence (AI) systems, based on von Neumann architecture and classical neural networks, have a number of fundamental limitations in comparison with the mammalian brain.
AI projects in which these limitations are overcome by bringing some brain features into the functioning and organization of computing systems (Loihi, Tianjic, SpiNNaker, BrainScaleS, NeuronFlow, DYNAP, Akida, Mythic).
Can Holistic AI take over the world?
The simple answer is probably not in our lifetime but it a concern to experts in the field. Currently AI relies on powerful computers but they still require power and can be subject to failure and so you can simply pull the plug on AI. AI can degrade abilities and experiences that people consider essential to being human. For example, humans are judgment-making creatures. People rationally weigh particulars and make daily judgment calls at work and during leisure time about whom to hire, who should get a loan, what to watch, and so on.
Will Holistic AI take over my Job?
diagnosing disease, translating languages, and providing customer service—and it’s improving fast. This is raising reasonable fears that AI will ultimately replace human workers throughout the economy. But that’s not the inevitable, or even most likely, outcome. Never before have digital tools been so responsive to us, nor we to our tools. While AI will radically alter how work gets done and who does it, the technology’s larger impact will be in complementing and augmenting human capabilities, not replacing them.
Why are some people worried about Holistic AI?
Some people are worried about Holistic AI for several reasons: 1. Job displacement: One concern is that AI systems could automate many jobs, leading to unemployment or job displacement for certain industries or job roles. As AI systems become more capable, there is a fear that they could replace human workers in various sectors, leading to economic and social implications. 2. Lack of control: There is a concern that as AI systems become more advanced and autonomous, humans may lose control over them. This raises questions about who is responsible for the actions and decisions made by AI systems and what safeguards are in place to prevent misuse or unintended consequences. 3. Ethical considerations: AI systems are only as good as the data they are trained on. If the training data is inaccurate the AI system could show bias and make wrong decisions.
Is there any legislation around Holistic AI?
Regulation of artificial intelligence (AI) is emerging around the globe, particularly in the US and EU, where laws have been proposed and adopted to manage the risks that AI can pose. However, the UK government is yet to propose any AI-specific regulation. Instead, individual departments have published a series of guidance papers and strategies to provide a framework for those using and developing AI within the UK.
Thanks.
Visit Kuldeepuk-kohli.com or infoai.uk for more Information.