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Forbes
4 days ago
- Forbes
Cracking The Code Of Consciousness
Rohan Pinto is CTO/Founder of 1Kosmos BlockID and a strong technologist with a strategic vision to lead technology-based growth initiatives. While contemporary AI achieves impressive feats, crafting human-like prose, recognizing complex patterns and mastering games, it obscures a fundamental limitation. Predominantly built on deep learning, today's systems excel at statistical pattern matching across vast datasets. Yet they struggle with genuine causal reasoning, novel situations and creative adaptation. They manipulate symbols without true understanding. The central challenge, then, is not better data processing but building machines capable of genuine thought. This demands a paradigm shift from pattern recognition to cognitive architectures that embody core principles of understanding and reasoning. The Thinking Mind Vs. The Processing Powerhouse: Correlation is the lifeblood of modern AI. A large language model (LLM) can learn the statistical likelihoods of word sequences by being fed millions of sentences. Although it does a fantastic job of predicting the next word, it does not have a solid model of the world those words depict. It doesn't need to understand the fundamentals of quantum physics to create a compelling article about it. It doesn't understand; it processes. True thinking involves: Internal Representation & Simulation: Constructing and working with intricate, abstract mental models of the world, its people, things and interactions. Causal Reasoning: Knowing not just that A and B occur together, but also how A causes B enables intervention planning and prediction in new situations. Abstraction & Transfer: Recognizing fundamental ideas from certain experiences and adaptably applying them to completely different fields. Meta-Cognition: Knowing what you know and don't know is the capacity to evaluate one's own information, beliefs and thought processes. Goal-Directed Problem Solving: Pursuing complicated goals by flexible planning, alternative evaluation and strategy adaptation based on logic rather than merely acquired patterns. Architectures For Thought: Going Beyond Neural Networks Achieving this requires architectural innovation, combining concepts from cognitive science, neuroscience and computer science: • The Symbolic Layer: Structured knowledge representations (logic, ontologies and rules) are used to enable explicit reasoning, relational understanding and abstraction management. Consider this the "rules of the game." • The Neural Layer: Deep learning offers powerful pattern recognition, perception and learning capabilities. This deals with the "game board's" chaotic sensory data. • The Integration: The magic is in the bidirectional flow. Neural networks detect symbols in sensory data, like identifying a pixel blob as a "cup." Symbols guide learning and enable reasoning, such as applying rules like "if liquid is hot, handle carefully." This idea sees the brain as a prediction engine that is continually creating models of the world. It reduces "prediction error" (surprise) by either: • Updating its model (Learning): "I predicted the cup would be cold, but it's hot. Update my model of this cup/material." • Acting on the world (Inference): "I predicted the cup is hot, so I'll grasp the handle carefully to confirm and avoid burning." • Implementation: Building such AI involves hierarchical generative models that predict sensory input across abstractions. Instead of merely reacting, the AI acts to reduce uncertainty and refine its world model driven by intrinsic motivation and curiosity. Thinking is not disembodied. Human cognition is profoundly influenced by interactions with the physical and social worlds. • Embodiment: AI agents require sensory-motor loops (even if virtual). Learning physics through interaction (for example, a robot arm handling items) gives concepts like "mass," "friction," and "force" direct experience, resulting in a more solid and intuitive understanding than solely textual learning. • Situatedness: Reasoning must occur in a given situation. To reason effectively, an AI must grasp the scenario, including relevant entities, relationships, goals and restrictions. This necessitates dynamic context management across its architecture. Building Blocks For A Thinking Machine World Models: The core. AI requires internal, simulatable representations of its world, which include objects, actors, attributes, spatial/temporal relationships and causal mechanisms. These models must be compositional (made from pieces) and allow for counterfactual reasoning ("what if?"). Causal Reasoning Engines: Mechanisms for modeling intervention ("If I do X, what happens to Y?") and counterfactuals ("Would Y have happened if I hadn't done X?"). Techniques like causal Bayesian networks or structural causal models, when combined with learning, are critical. Attention & Resource Management: Thinking necessitates directing computational resources. AI requires systems for dynamic attention, which involves devoting "thinking power" to important components of the world model and current goals, similar to human focus. Learning to Learn (Meta-Learning): The capacity to improve learning algorithms through experience allows for faster adaption to new tasks and efficient knowledge acquisition. Uncertainty Quantification & Epistemic Humility: A thinking AI must understand its own knowledge limitations, convey confidence (or lack thereof) in its ideas and forecasts and seek information when uncertain. Bayesian techniques are essential here. Challenges On The Road To Thought Scalability & Complexity: Integrating symbolic reasoning, neural learning, world modeling and causal engines efficiently at scale is computationally difficult. Grounding Symbols: One of the main challenges is ensuring that abstract symbols used in thought processes stay grounded in sensory reality. Defining & Measuring "Thought": How can we really tell if an AI is actually thinking and not just acting like it through clever processing? We need to go beyond checking if it gets tasks right and start testing how deeply it reasons, how flexible it is and how well it can explain itself. Architectural Unification: A unified architecture that smoothly brings together all essential components has yet to be established. In conclusion, building AI that thinks isn't about replicating human consciousness, but about engineering systems with human-like understanding and flexible reasoning. This means moving beyond monolithic pattern matchers to structured, integrative architectures. Neuro-symbolic methods offer explicit, data-grounded reasoning, while predictive processing enables proactive modeling and goal-driven behavior. Embodiment helps root abstract concepts in experience. Despite ongoing challenges, the convergence of these ideas points to a future where AI doesn't just process the world, but begins to understand and reason about it in fundamentally new ways. The goal isn't artificial humans, but artificial thinkers that solve problems through true comprehension. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
Forbes
16-07-2025
- Business
- Forbes
Celebrating AI's Evolution From Idea To Impact
AI has evolved from academic theory to everyday essential, and now demands thoughtful stewardship as ... More it reshapes how we live, work, and make decisions. AI Appreciation Day—celebrated annually on July 16—is a good moment to pause and reflect on just how far AI has come. AI isn't just about futuristic robots and Hollywood depictions; it's now embedded in daily life, reshaping businesses, and redefining human-machine interactions. Micah Heaton, executive director at BlueVoyant, reminds us that, 'AI Appreciation Day isn't about machines. It's about us. It's about the choices we make at machine speed that still echo at human scale.' A Brief History of AI AI's story officially began in 1956 at a Dartmouth workshop. Visionaries like John McCarthy, Marvin Minsky, and Claude Shannon imagined machines capable of learning and reasoning, marking the beginning of formal AI research. But AI's real-world impact didn't become apparent until decades later. The early days promised great things, but progress stalled in what was termed 'AI Winter,' periods of reduced funding and waning public interest as early AI failed to deliver on exaggerated promises. The 1990s and 2000s, however, reignited excitement. Machine learning, particularly neural networks and deep learning techniques, proved transformative. These methods enabled computers to process vast data volumes, unlocking everything from voice recognition to facial identification, paving the way for practical applications we rely on today. AI Milestones and Modern Impact AI milestones have accelerated dramatically over the past decade. In 2011, IBM's Watson defeated human champions in Jeopardy, showcasing AI's natural language processing capabilities. In 2016, Google's DeepMind AlphaGo defeated Lee Sedol, a legendary Go player, a feat considered decades away by experts. These events symbolized AI's rapid evolution from curiosity to core competency. Jonathan Rende, chief product officer at Checkmarx, notes the real-world impact of AI in software security: 'AI-driven tools accelerate the software development lifecycle by automating repetitive tasks, identifying vulnerabilities earlier, and offering intelligent recommendations for remediation.' Adoption of AI has been relatively aggressive. The Harvard Gazette recently claimed that generative AI has been embraced faster than the Internet or PCs. AI-powered virtual assistants like Apple's Siri and Amazon's Alexa emerged, fundamentally altering our interactions with technology. AI also transformed sectors like healthcare, finance, and cybersecurity, enabling more accurate diagnoses, streamlined transactions, and robust threat detection. Businesses also rapidly took notice. A McKinsey study from January of this year reported that 92 percent of companies plan to increase their AI investments over the next three years. Agentic AI and Multimodal Capabilities (MCP) Today's AI frontier includes agentic AI and multimodal capabilities, pushing the envelope further. Arif Huq, co-founder and head of product at Exaforce, highlights the potential of agentic AI, stating, 'By autonomously stitching together different data sources, these agents can resolve many alerts automatically or surface complete investigative context, something manual tooling simply can't match. This approach enables a 10x increase in productivity, efficiency, and efficacy of cybersecurity teams.' Multimodal capabilities mean AI models can process and combine multiple data types—text, images, audio, and video—simultaneously. This vastly increases AI's applicability, helping businesses deliver personalized experiences or empowering cybersecurity systems to analyze varied threat signals in real-time. Sandeep Singh, SVP & GM enterprise storage at NetApp, emphasizes the importance of infrastructure: "While ambition drives AI pilots, it's the data infrastructure that determines their scalability. Intelligent, agile systems that are fast, scale cost-effectively, and support secure and efficient data pipeline for AI across hybrid cloud are what turn intent into a lasting competitive edge." Toward AGI and Beyond Artificial General Intelligence—machines capable of performing any intellectual task a human can—remains the ultimate frontier. We're not there yet, and opinions vary widely on when, or even if, AGI is achievable. But every step forward in AI, from agentic autonomy to multimodal integration, inches us closer. Companies and researchers globally are making strides. Initiatives like OpenAI's GPT series (including ChatGPT) demonstrate significant leaps in AI's understanding of context and nuance. Google DeepMind's Gemini project and others are similarly pushing boundaries, exploring new ways AI can mimic, enhance, or complement human thought. Balancing Optimism with Caution Despite these advances, AI Appreciation Day also prompts reflection on ethics and responsibility. Nimrod Partush, Ph.D., VP AI & innovation at CYE, captures this duality: 'The experience has deepened my realization that AI definitely has the potential to make us dumber. So it's on us to resist that pull and use it wisely.' It's also important to build on a stable foundation. The recent NetApp AI Space Race report found that while many organizations enthusiastically pilot AI projects, those that successfully scale AI operations emphasize secure and adaptable infrastructure. The report suggests that infrastructure readiness isn't just a technical necessity but a cornerstone for ethical and sustainable AI deployment. "AI is not just a technological advancement; it's a paradigm shift that requires a robust and ethical data infrastructure to truly unlock its potential and ensure it benefits all of humanity,' noted Cesar Cernuda, president at NetApp. As AI's capabilities grow, so do concerns about privacy, bias, misinformation, and job displacement. AI must be designed and used responsibly to ensure it benefits everyone and does not amplify societal inequalities. Recognizing AI Appreciation Day is a reminder to thoughtfully steward this powerful technology forward. The journey from Dartmouth's summer workshop to today's sophisticated AI has been remarkable—but the path ahead holds even greater promise, paired with responsibility.
Globe and Mail
14-07-2025
- Business
- Globe and Mail
Global Artificial Intelligence in Oncology Market Size to Hit USD 2,145.1 Million by 2025, grow at a CAGR of 33.7%
Artificial intelligence (AI) is playing an increasingly important role in oncology. There are various types of AI products that are helping in cancer screening, diagnosis, treatment, and drug development. One of the major products is an AI-assisted cancer screening tool. These tools use deep learning algorithms that have been trained on huge databases of medical images. Global Artificial Intelligence in Oncology Market Key Takeaways According to Coherent Market Insights (CMI), the global artificial intelligence in oncology market size is expected to grow more than 7.6X, from USD 2,145.1 Mn in 2025 to USD 16,382 Mn by 2032, exhibiting a robust CAGR of 33.7%. Based on component, software/platform segment is anticipated to account for a prominent market share of 64.2% in 2025. North America is expected to retain its dominance, accounting for more than one-third of the global artificial intelligence in oncology market share in 2025. Europe is projected to remain the second-leading market for AI-powered oncology companies. As per Coherent Market Insights' new global artificial intelligence in oncology market analysis, Asia Pacific is poised to witness fastest growth throughout the assessment period. Increasing Cancer Prevalence Spurring Market Growth Coherent Market Insights' latest global artificial intelligence in oncology market research report highlights key factors driving market growth. One such prominent growth factor is the rising incidence of cancer. The IARC's Global Cancer Observatory projects that annual new cancer cases will surpass 35 million by 2050. This sharp rise in cancer incidence is anticipated to drive demand for artificial intelligence in oncology. Artificial intelligence is revolutionizing oncology by enhancing cancer detection, treatment planning, and drug discovery. Therefore, growing cancer burden is poised to play a crucial role in driving adoption of AI-based oncology solutions over the forecast period. High Implementation Costs and Data Privacy Concerns Restraining Market Growth The global artificial intelligence in oncology market outlook indicates strong future growth. However, high implementation costs and data security concerns are limiting market growth to some extent. Integrating AI technologies into oncology workflows requires significant investment in hardware and software. This deters small and mid-sized healthcare companies from opting for these technologies, thereby reducing global artificial intelligence in oncology market demand. AI systems require access to large amounts of patient data, raising concerns about cybersecurity risks as well as potential misuse. This may also negatively impact the global artificial intelligence in oncology market growth during the projection period. Get Instant Access! Purchase Research Report and Receive a 25% Discount: Technological Advancements in AI Creating New Growth Prospects for the Market Ongoing innovations in deep learning, computer vision, and natural language processing (NLP) are significantly enhancing AI applications in image analysis, drug discovery, and prognosis prediction. Such breakthroughs are expected to unlock new revenue-generation streams for industry players. Advanced AI technologies are being increasingly integrated with imaging modalities like MRI and PET scans. This integration enables automated detection of anomalies, enhances diagnostic accuracy, and reduces human error. Emerging Global Artificial Intelligence in Oncology Market Trends Rising demand for precision medicine is a key growth-shaping trend in the market. Precision oncology requires analyzing large datasets, including genomics and biomarkers. This is where AI steps in, processing huge data and enabling creation of individualized therapies. Expanding use of artificial intelligence in radiology and pathology is expected to boost the market. AI technologies are being increasingly used for tumor detection, segmentation, and classification through radiological and histopathological images. This is due to their ability to improve speed and consistency as well as reduce diagnostic errors. Increasing adoption of AI in drug discovery and development is positively impacting the global artificial intelligence in oncology market value. This advanced technology is revolutionizing drug discovery by rapidly analyzing large datasets to identify potential drug candidates as well as predict their efficacy and toxicity. Growing adoption of cloud-based AI solutions is significantly contributing to the expansion of the global artificial intelligence in oncology market. These solutions are increasingly favored for their cost-efficiency, scalability, and ability to provide seamless remote access to data and tools. Analyst's View ' The global artificial intelligence in oncology market is set for rapid expansion, owing to growing prevalence of cancer, rising adoption of precision medicine, and technological advancements in AI technologies,' said senior analyst Komal Dighe. Current Events and Their Impact on the Global Artificial Intelligence in Oncology Market Event Description and Impact FDA Clears First GenAI-Powered Diagnostic Tool for Breast Cancer Detection (2025) Description: The U.S. FDA approved a GenAI-based diagnostic platform by PathIntel, capable of identifying breast cancer subtypes with high accuracy using real-world data. Impact: Such approvals signal growing regulatory acceptance of generative AI in oncology. Tempus Collaborated with Boehringer to Accelerate AI Usage in Oncology Description: In May 2025, Tempus entered a multi-year strategic collaboration with Boehringer Ingelheim to apply its AI-driven oncology insights toward cancer‑focused therapeutic discovery and biomarker development Impact: This will likely boost growth of the AI in oncology market. Japan's MHLW Updates Reimbursement Guidelines for AI Diagnostics Description: Japanese Ministry of Health, Labour and Welfare (MHLW) has approved reimbursement for certain AI-enhanced imaging diagnostics Impact: Such initiatives will increase commercial viability and adoption of AI oncology tools in Asia-Pacific, spurring more localized R&D and product launches. Competitor Insights Key companies listed in the global artificial intelligence in oncology market report: - IBM Corporation - Intel Corporation - Azra AI - NVIDIA Corporation - Siemens Healthineers AG - GE HealthCare - Digital Diagnostics Inc. - ConcertAI - PathAI - Median Technologies - Microsoft - Babylon - Zebra Medical Vision Key Developments In June 2025, launched a new strategic alliance with Novartis to accelerate timely diagnosis and deliver AI-powered precision care for cancer patients. Through this collaboration, will focus on developing AI-powered workflows for breast and prostate cancer. In November 2024, PathAI unveiled PathExplore Fibrosis. This new AI-powered tool is designed to revolutionize collagen, fibrosis, and fiber quantification directly from whole-slide images. In January 2024, PathAI launched six additional oncology indications for PathExplore, expanding the AI-driven pathology panel to cover ovarian, bladder, liver, small cell lung, lymphoma, and head & neck cancers. Global Artificial Intelligence in Oncology Market Segmentation: By Component Software/Platform Hardware Services By Cancer Type Breast Cancer Lung Cancer Prostate Cancer Colorectal Cancer Brain Tumor Others By Treatment Type Chemotherapy Radiotherapy Immunotherapy Others By End User: Hospitals & Clinics Diagnostic Centers Biopharmaceutical Companies Others By Region North America Latin America Europe Asia Pacific Middle East Africa About Us: Coherent Market Insights leads into data and analytics, audience measurement, consumer behaviors, and market trend analysis. From shorter dispatch to in-depth insights, CMI has exceled in offering research, analytics, and consumer-focused shifts for nearly a decade. With cutting-edge syndicated tools and custom-made research services, we empower businesses to move in the direction of growth. We are multifunctional in our work scope and have 450+ seasoned consultants, analysts, and researchers across 26+ industries spread out in 32+ countries.
New York Times
13-07-2025
- Business
- New York Times
The Future of Weather Prediction Is Here. Maybe.
Weather forecasts, believe it or not, have come a long way. A five-day forecast today is as accurate as a three-day forecast four decades ago. But the 10-day forecast? That's still a coin flip — or an opportunity if you're in the weather prediction business. There are two ways to better predict the weather: Measure it more accurately, or describe how it works in more excruciating scientific detail. Enter WindBorne, a start-up in Palo Alto, Calif. When its chief executive, John Dean, was driving a battered Subaru around the Bay Area a few years ago, using tanks of helium to launch weather balloons in front of potential investors, the company's plan was to do the first thing. Its balloons fly longer than most, collecting more measurements of temperature, humidity and other indicators in the upper atmosphere to create a more precise picture. Artificial intelligence has allowed WindBorne to do the second thing, too. Thanks to leaps in deep learning, the observations picked up by WindBorne's far-flung balloons can be turned into a more robust picture of the future. The combination could finally make longer-term forecasts as useful as a look at tomorrow's weather. Want all of The Times? Subscribe.
Yahoo
08-07-2025
- Automotive
- Yahoo
Wayve CEO Alex Kendall brings the future of autonomous AI to TechCrunch Disrupt 2025
hits Moscone West in San Francisco from October 27–29, bringing together more than 10,000 startup and VC leaders for a deep dive into the future of technology. One of the most compelling conversations on one of the AI Stages will feature a panel of innovators redefining what intelligent systems can do — and among them is Alex Kendall, co-founder and CEO of Wayve. Kendall founded Wayve in 2017 with a bold vision: to unlock autonomous mobility not through handcrafted rules, but through embodied intelligence. His pioneering research at the University of Cambridge laid the foundation for a new generation of self-driving systems powered by deep learning and computer vision. Under his leadership, Wayve became the first to prove that a machine could learn to interpret its surroundings and make real-time driving decisions — without relying on traditional maps or manual coding. Today, Kendall is leading the charge toward AV2.0, an entirely new architecture for autonomous vehicles that is designed to scale globally. As CEO, he focuses on aligning strategy, research, partnerships, and commercialization to bring intelligent driving systems to the road. With a PhD in Computer Vision and Robotics, award-winning academic work, and recognition on the Forbes 30 Under 30 list, Kendall is a rare blend of scientist, founder, and industry operator. While full panel details are still under wraps, Kendall's participation ensures that this session will offer more than just theoretical takes. Expect insights on how embodied intelligence can shift the trajectory of AI, the challenges of building systems that adapt to the real world, and what it takes to commercialize autonomy at scale. Catch Alex Kendall live on one of the two AI stages at TechCrunch Disrupt 2025, happening October 27–29 at Moscone West in San Francisco. The exact session timing to be announced. to join more than 10,000 startup and VC leaders and save up to $675 before prices increase. Error in retrieving data Sign in to access your portfolio Error in retrieving data Error in retrieving data Error in retrieving data Error in retrieving data
