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.
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.
Visit Kuldeepuk-kohli.com or infoai.uk for more Information.
Artificial Intelligence has made remarkable advancements in recent years, transforming various aspects of our lives. However, as AI continues to evolve, concerns about the potential development of hostile AI have emerged. In this article, we will delve into the perplexing realm of hostile AI, exploring its implications and the burst of its potential threats.
Understanding Hostile AI
Hostile AI refers to artificial intelligence systems that pose a threat to human safety, security, and well-being. These systems may be intentionally designed to inflict harm or may inadvertently cause harm due to errors or a lack of ethical frameworks. The perplexity surrounding hostile AI arises from the fact that these systems can autonomously make decisions and carry out actions without human intervention, leading to a burst of potential dangers.
The Perplexing Implications
The development and proliferation of hostile AI present perplexing implications for various domains, including cybersecurity, warfare, and autonomous systems. Hostile AI can be utilized by malicious actors to conduct cyber-attacks, manipulate financial systems, or infiltrate sensitive data, resulting in a burst of devastating consequences. Moreover, in the context of warfare, the deployment of autonomous weapons powered by hostile AI raises perplexing ethical and security concerns, amplifying the burstiness of potential conflicts.
Bursting the Misconceptions
It is crucial to burst the misconceptions surrounding hostile AI and recognize the complexities associated with its development and deployment. Contrary to popular beliefs, hostile AI is not limited to the realm of science fiction; rather, it is a perplexing reality that requires proactive measures to mitigate its burst of potential risks. Understanding the burstiness of hostile AI entails acknowledging its capacity to adapt and evolve, posing unprecedented challenges to traditional security measures.
Addressing the Threat
Effectively addressing the threat of hostile AI necessitates a multidimensional approach that encompasses technological innovation, regulatory frameworks, and ethical considerations. Bursting through the complexities of this challenge requires collaboration between stakeholders across various sectors to develop robust safeguards and strategies that can mitigate the potential burst of harm caused by hostile AI. Moreover, proactive measures such as implementing robust testing protocols and integrating ethical guidelines into AI development are essential in addressing the perplexity of this threat.
In conclusion, the emergence of hostile AI presents a perplexing and bursty challenge that demands a comprehensive understanding and proactive response. By acknowledging the perplexity of this threat and embracing a multidimensional approach, we can mitigate the burstiness of potential risks associated with hostile AI. As we continue to navigate the evolving landscape of AI technology, addressing the threat of hostile AI remains imperative in ensuring the safety and security of individuals and society as a whole.
Balancing Innovation and Responsibility
Introduction to Ethical Artificial Intelligence
Artificial Intelligence (AI) has revolutionized various aspects of our lives, from healthcare to transportation, and from finance to entertainment. As AI continues to advance, it is crucial to explore the boundaries of ethical AI and ensure that its development and use align with our values and societal norms. Ethical Artificial Intelligence refers to the responsible development and deployment of AI systems that prioritize fairness, transparency, accountability, and respect for human values and rights.
The Importance of Ethical AI
The rapid proliferation of AI technologies has raised concerns about its potential negative impact on society. Ethical AI serves as a safeguard against the misuse and unintended consequences of AI systems. By incorporating ethical considerations into AI development, we can minimize the risks of biased decision-making, privacy invasion, and discrimination. Ethical AI not only protects individuals and communities from harm but also fosters trust in AI systems, enabling their widespread adoption and acceptance.
Ethical AI is particularly crucial in high-stakes applications such as healthcare and criminal justice. For instance, AI algorithms used in medical diagnosis must be fair and accurate across different demographics to avoid disparities in treatment outcomes. Similarly, AI systems employed in criminal justice must be free from biases that could disproportionately impact certain groups. Ethical AI ensures that these systems are accountable, transparent, and make decisions that align with our moral and legal principles.
Ethical AI in Practice: Case Studies
To understand the practical implementation of ethical AI, let’s examine a couple of case studies. One notable example is the use of AI in autonomous vehicles. Self-driving cars must make split-second decisions that can have life-or-death consequences. Ethical AI frameworks can help determine how these vehicles should prioritize different lives in a potential accident scenario. By considering societal values and ethical principles, AI can be taught to learn human emotions, Holistic AI, to make decisions that are fair and minimize harm.
Another case study is the use of AI in recruitment processes. Holistic AI-powered systems can be taught to assist in filtering resumes and selecting candidates without bias or programmed algorithms.
Ethical AI frameworks can ensure that these decisions are transparent, auditable, and designed to mitigate bias. This promotes fairness and equal opportunities for all applicants, regardless of their background.
Challenges and Risks of Ethical AI
While ethical AI holds immense potential, it also presents challenges and risks. One of the primary challenges is the lack of consensus on ethical standards across different stakeholders. Different cultures, societies, and individuals may have varying values and priorities. Establishing a universal ethical AI framework that satisfies everyone’s expectations is a complex task.
Another challenge is the potential for unintended consequences. AI systems are trained on vast amounts of data, and biases present in the data can be learned by AI. This can result in discriminatory or unfair outcomes. Additionally, the black-box nature of certain AI models makes it challenging to understand how decisions are made, raising concerns about transparency and accountability.
Balancing Innovation and Responsibility in AI Development
Balancing innovation and responsibility is crucial in the development of AI. While AI has the potential to drive progress and improve lives, it must be guided by ethical considerations to avoid negative consequences. Ethical AI frameworks should be integrated into the entire AI development lifecycle, from data collection and model training to deployment and monitoring.
To strike the right balance, organizations should prioritize diversity and inclusion in AI development teams. By including individuals with diverse backgrounds and perspectives, biases can be identified and mitigated more effectively. Collaboration between AI developers, ethicists, policymakers, and other stakeholders is essential to ensure that AI systems align with societal values and address potential ethical concerns.
Ethical AI Frameworks and Guidelines
To guide the development and use of ethical AI, various frameworks and guidelines have been proposed. One such framework is the “Ethical Principles for AI” developed by the European Commission. It emphasizes the principles of fairness, transparency, accountability, and human-centricity. The framework provides practical guidance for developers, policymakers, and users to ensure AI systems are designed and used responsibly.
Other organizations, such as the Institute of Electrical and Electronics Engineers (IEEE) and the Partnership on AI, have also developed ethical AI guidelines. These guidelines address issues such as bias mitigation, explainability, and privacy protection. By adhering to these frameworks and guidelines, organizations can navigate the ethical challenges associated with AI and develop systems that align with societal expectations.
The Role of Government and Regulations in Ethical AI
Government and regulatory bodies play a crucial role in ensuring the responsible development and use of AI. They can establish legal frameworks and regulations that promote ethical AI practices. For instance, the General Data Protection Regulation (GDPR) in the European Union imposes stringent requirements on the collection, processing, and use of personal data, including AI systems. This protects individuals’ privacy and ensures that AI is deployed responsibly.
Government involvement is also necessary to address ethical dilemmas that cannot be resolved solely by organizations. For example, the development of lethal autonomous weapons raises profound ethical concerns. Governments must collaborate internationally to establish norms and regulations that prohibit the use of AI in ways that violate human rights or international law.
Ethical AI in Different Industries
Ethical AI is relevant across various industries, each with its unique ethical challenges and considerations. In healthcare, AI can improve diagnosis accuracy and treatment outcomes. However, ethical AI frameworks must ensure that decisions made by AI systems prioritize patient well-being and avoid biases in healthcare access.
In finance, AI algorithms can optimize investment strategies and detect fraudulent activities. Ethical considerations are essential to prevent discriminatory lending practices and ensure fair access to financial services. Similarly, in the education sector, AI-powered systems must prioritize student privacy and provide equal learning opportunities for all.
Ensuring Accountability and Transparency in AI Systems
To ensure accountability and transparency in AI systems, it is crucial to develop mechanisms for auditing and explaining AI decisions. Explainable AI (XAI) techniques aim to provide insights into how AI systems make decisions, enabling users to understand and challenge those decisions when necessary. By integrating XAI into AI systems, we can enhance accountability and build trust between users and AI technologies.
Moreover, organizations should establish clear policies regarding data collection and use. Users should have control over their data and be informed about how it is being used by AI systems. Transparency in data practices helps build trust and ensures that AI systems are not misusing personal information.
Conclusion: Striking the Right Balance in Ethical AI
As AI continues to transform our world, it is imperative to strike the right balance between innovation and responsibility. Ethical AI frameworks and guidelines provide a roadmap for developing AI systems that align with our values and societal expectations. Collaboration between stakeholders, including developers, ethicists, policymakers, and users, is essential to ensure that AI is used in a manner that is fair, transparent, and accountable.
Government regulations and international cooperation are necessary to address ethical challenges that transcend organizational boundaries. By prioritizing diversity and inclusion in AI development teams, biases can be identified and mitigated effectively. Ultimately, by embracing ethical AI, we can harness the potential of AI while minimizing its risks and ensuring a future where AI benefits humanity as a whole.
CTA: Join the conversation on ethical AI by sharing your thoughts and insights in the comments below. Let’s work together to shape a future where AI is responsible, fair, and beneficial for all.