Top AI Predictions for the Future

I always share a post each year talking about my predictions in technology. Here are my general technology trends for 2025. 🔺 Wider Adoption of Generative AI 🔹 Domain-specific models: We’ll see more specialized generators trained on targeted data (e.g., legal, medical, scientific) that can produce highly accurate and context-specific content. 🔹 Hybrid approaches: Enterprises will use generative AI alongside rule-based or traditional ML methods to achieve more reliable outcomes, minimizing hallucinations and biases. 🔺 Rise of Multimodal Systems 🔹 Unified AI experiences: Instead of siloed text, image, audio, and video models, we’ll see integrated systems that seamlessly handle multiple data types. This leads to richer applications, from next-gen customer support to advanced robotics. 🔹 Context-aware processing: AI will better understand real-world context, combining visual, audio, and textual cues to offer smarter responses and predictions. 🔺 Advances in Explainability and Trust 🔹 Regulatory frameworks: With stricter AI regulations on the horizon, model explainability and audibility will become core requirements, especially in finance, healthcare, and government. 🔹 AI “nutrition labels”: Standardized ways of conveying model biases, training datasets, and reliability will help build user trust and improve transparency. 🔺 Edge and On-Device AI 🔹 Lower latency, better privacy: More powerful AI models will run directly on phones, wearables, and IoT devices, reducing dependence on the cloud for tasks like speech recognition, image processing, and anomaly detection. 🔹 Specialized hardware: Continued investment in AI accelerators, TPUs, and neuromorphic chips will enable high-performance AI at the edge. 🔺 Human-AI Teaming and Augmented Decision-Making 🔹 Decision intelligence platforms: AI will shift from purely providing recommendations to working interactively with humans to explore complex problems—reducing cognitive load, but keeping humans in the loop. 🔹 Collaborative coding and content creation: AI co-pilots will expand from code generation and text drafting to more sophisticated collaboration, shaping design, research, and strategic planning. 🔺 Rapid Growth of AI as a Service (AIaaS) 🔹 “No-code” and “low-code” tools: Tools that allow non-technical users to deploy custom AI solutions will proliferate, lowering barriers to entry and accelerating adoption across industries. 🔺 Emphasis on Ethical and Responsible AI 🔹 Bias mitigation: Tools and techniques to detect and reduce bias will grow more advanced, spurred by public scrutiny and regulatory demands. 🔹 Standards for accountability: Organizations will create ethics boards and formal guidelines to ensure AI alignment with corporate values and social responsibility. 🔺 Quantum Computing Experiments 🔹 Hybrid quantum-classical models: Though still early-stage, breakthroughs in quantum hardware could lead to specialized quantum-assisted AI algorithms.

The next wave of AI transformation is here – and it’s not just about language-based models anymore. The real breakthroughs are happening now with Large Quantitative Models (LQMs) and cutting-edge quantum technologies. This seismic shift is already unlocking game-changing capabilities that will define the future: Materials & Drug Discovery – LQMs trained on physics and chemistry are accelerating breakthroughs in biopharma, energy storage, and advanced materials. Quantitative AI models are pushing the boundaries of molecular simulations, enabling scientists to model atomic-level interactions like never before. Cybersecurity & Post-Quantum Cryptography – AI is identifying vulnerabilities in cryptographic systems before threats arise. As organizations adopt quantum-safe encryption, they’re securing sensitive data against both current AI-powered attacks and future quantum threats. The time to act is now. Medical Imaging & Diagnostics – AI combined with quantum sensors is revolutionizing medical diagnostics. Magnetocardiography (MCG) devices are providing more accurate cardiovascular disease detection, with potential applications in neurology and oncology. This is a breakthrough that could save lives. LQMs and quantum technologies are no longer distant possibilities—they’re here, and they’re already reshaping industries. The real question isn’t whether these innovations will transform the competitive landscape—it’s how quickly your organization will adapt.

Top 6 AI Predictions for 2024 from IBM 𝟭. 𝗖𝘂𝘀𝘁𝗼𝗺𝗶𝘇𝗲𝗱 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗔𝗜 Tailored generative AI applications are becoming vital for businesses, offering personalized solutions and enhancing customer interactions. 𝟮. 𝗢𝗽𝗲𝗻 𝗦𝗼𝘂𝗿𝗰𝗲 𝗔𝗜 𝗠𝗼𝗱𝗲𝗹𝘀 The rise of open-source AI, like IBM's collaboration with NASA, democratizes AI technology, notably in climate research. 𝟯. 𝗔𝗣𝗜-𝗗𝗿𝗶𝘃𝗲𝗻 𝗔𝗜 𝗮𝗻𝗱 𝗠𝗶𝗰𝗿𝗼𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀 APIs are simplifying AI application development, boosting productivity in various sectors. 𝟰. 𝗔𝗜 𝗮𝘀 𝗮 𝗡𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗣𝗿𝗶𝗼𝗿𝗶𝘁𝘆 Countries are increasingly recognizing AI's strategic importance, leading to significant advancements and regulations like the EU AI Act. 𝟱. 𝗠𝘂𝗹𝘁𝗶𝗺𝗼𝗱𝗮𝗹 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 The integration of text, speech, and images is offering contextually richer AI interactions. 𝟲. 𝗔𝗜 𝗦𝗮𝗳𝗲𝘁𝘆 𝗮𝗻𝗱 𝗘𝘁𝗵𝗶𝗰𝘀 The focus on AI ethics grows, with alliances like the one between IBM and Meta fostering responsible AI innovation. These trends are more than predictions; they're a roadmap. We are collaborating with customers to leverage AI's potential in creating a more efficient, innovative, and inclusive future.

Wondering what's happening next for AI and LLMs? Here is exactly what top AI research Richard Socher says: 1. What's driving AI model improvements now and in the future? Current driver: Bigger models and more training data. Next few years: Focus on improving algorithms/reasoning. Long-term: Improved simulations across real-world scenarios. "Anything you can simulate, you can eventually solve with AI." 2. Will synthetic data solve the data shortage for LLMs? Synthetic data may not be ideal for natural language models. Human-generated content still crucial for training/improvement. World changes require constant new data input (i.e. new president). 3. Will we see diminishing returns from adding more training data? Logarithmic flattening of improvements from more data coming. Magnitude more data needed for small benchmark improvements. Balance needed between data, algorithms, and hardware advances. 4. How will AI capabilities expand in the coming years? Integrating logical reasoning to reduce today's silly mistakes. Developing advanced simulations for language and characters. Applying AI to complex scientific challenges like cancer research. P.S. How do you keep an eye on current AI developments?

Top 5 AI Trends to Watch in 2024 As 2024 unfolds, the AI landscape is poised for pivotal advancements. Here are my top 5 predictions, blending various industry insights: The Emergence of AI-Generated Video and Multimodal AI Models: This year, expect AI video models to mature into sophisticated products, paralleling the evolution of text and image models. Additionally, multimodal AI models capable of processing text, images, audio, and video will become more intuitive and commonplace, led by advancements in models like GPT-4. AI Expertise Becomes a Core Competency in the Job Market: With AI's expanding influence, having AI skills will be as essential as traditional tools like Excel. Morgan Stanley predicts AI's impact on 40% of the workforce, and employers are increasingly seeking AI-qualified talent. Advancements in AI for Mobile Technology and Specialized Models: 2024 will witness significant progress in AI applications for smartphones and the rise of smaller, more specialized AI models like Microsoft's PHI-2. These developments will make AI more versatile and accessible across various industries. Open Source AI Gains Prominence Over Closed Models: Open source AI is set to surpass closed models in popularity and innovation. Companies like Meta and Huggingface are leading this shift, democratizing access to AI technology. Data Quality Takes Center Stage: As concerns around copyright and training materials increase, the focus on the quality of data used to train AI models will become more crucial than ever. Ensuring high-quality, ethically sourced data will be a key consideration in AI development. As we step into 2024, let's look forward to a year rich in AI-driven innovation and transformation. Happy New Year, and here’s to a trailblazing year ahead in the world of AI!

If you were hoping for a slowdown in AI innovation in 2025, the first 38 days of the year are proving that the space is only accelerating. My six predictions for AI and software engineering this year - backed by what we're seeing in the market today: 1. The LLM moat is shrinking - With DeepSeek approaching closed models and available for free, value is shifting to what you build on top. Basic LLM access is becoming more of a commodity - and that's good for innovation. 2. Enterprise AI will go vertical - The next wave isn't general-purpose models. It's specialized AIs trained on proprietary enterprise data. Every major industry will build domain-specific models on open source foundations. 3. Software engineering teams will grow, not shrink - Controversial take: AI making software development cheaper and more predictable will increase demand for engineers. Smart CTOs are using AI to tackle their feature backlog, not reduce headcount. 4. RAG trumps fine-tuning - Real-time context beats static training. The future is retrieval-first: lower costs, better security, instant updates. 5. Two AI-assisted programming paradigms evolve - Engineers will seamlessly switch between: Direct coding with AI assistance and Meta-programming through natural language. The key is having tools that maintain context across both modes. 6. AI agents for software get real - Beyond code completion and chat, AI will handle: Test generation, migrations, security scanning, documentation, more complex refactors. But with human oversight, not autonomously. Augment Code https://lnkd.in/eerVneuX

Based on current trends, I'd like to make 10 predictions about where #BigData, #DataScience, and #ArtificialIntelligence are heading in 2024. 1. 𝐈𝐦𝐦𝐞𝐫𝐬𝐢𝐯𝐞 AI will overlay reality with generated/ predicted layers and even create alternative realities in real time due to massive improvements in AI, AR/XR, and hardware, thus finally leaving the uncanny valley! Excited for Apple's Vision Pro headset!   2. Data 𝐟𝐚𝐛𝐫𝐢𝐜 technologies will continue to evolve and be adopted, replacing less practical and more rigid data warehousing solutions. 3. AI 𝐖𝐞𝐚𝐩𝐨𝐧𝐬 and AI-enabled crime will be newsworthy in 2024 such as high-profile attacks leveraging geopolitical events and major happenings like the 2024 US elections, as well as AI-enabled CaaS (Cybercrime as a service) to sabotage and defraud businesses and individuals. As a response, new cybersecurity professionals specializing in AI threats will emerge, and ZeroTrust will become the norm. 4. 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 #AI will keep pressing on with 3d scenes and entire movies, even with sound, created with text. Issues like intellectual property and bias will continue to tarnish the technology's reputation. And sadly, misinformation will be rampant. 5. 𝐔𝐗 for AI will become part of the discussion in 2024 as AI is embedded into more consumer products. 6. 𝐂𝐨𝐧𝐭𝐢𝐧𝐮𝐨𝐮𝐬 Intelligence. In 2024, adopting more flexible data warehouse technologies and greater emphasis on observability will naturally lead to a more real-time augmented analytics view of business operations. Changes in mindsets surrounding data will make executives more comfortable with a more flexible, nuanced, multi-layered, continuous view, especially when AI can find patterns that explain changes on a granular level. 7. 𝐃𝐈𝐘 𝐀𝐈 (name of my upcoming book!) We all saw how 2022-23 unleashed early-adopter aficionado types (many of whom transitioned from crypto). However, 2024 will be the year that a broader group of enthusiasts take AI into their own hands using cheap consumer hardware. 8. 𝐇𝐮𝐦𝐚𝐧-𝐜𝐨𝐦𝐩𝐮𝐭𝐞𝐫 interface. Models trained on neural activity will make it possible to automate many tasks with just the thought of performing it, from walking with an exoskeleton to generating artwork with a mental image. 9. 𝐐𝐀 in AI development is a natural extension of #ResponsibleAI as the technology matures because best practices are not strictly about ethics, but they are about ensuring quality and everything you associate with quality. In 2024, a more rigorous approach will reach the field. 10. 𝐒𝐮𝐬𝐭𝐚𝐢𝐧𝐚𝐛𝐢𝐥𝐢𝐭𝐲. As global conflict drags on, creating volatility in energy markets, data centers and the companies that rely on them will push for more sustainable solutions thanks to the availability of low-cost renewable energy and ever-more efficient computing technologies Regarding technology, I'm excited about what's in store for 2024. And I wish you all a New Year filled with joy! 😊 🎉 

What Will Artificial Intelligence Look Like in 2030? Artificial intelligence is advancing rapidly, but experts have varied opinions on its impact by 2030. Will AI spur economic growth, medical breakthroughs, and simplify daily life? Or will advancements lag behind expectations, leading to unmet promises and new challenges like job displacement and erosion of societal trust? Here are some expert predictions: 🤖 AI Integrated into Everyday Life: AI becomes a routine part of businesses and personal life, much like the internet today, but widespread adoption is gradual due to the slow pace of organizational and societal change. 🏥 Transforming Healthcare: AI tools significantly improve diagnostics and patient care by quickly analyzing medical data, assisting healthcare professionals, and automating administrative tasks to reduce workload. 🤝 Advanced Personal Assistants: Personalized AI agents, or "Personal Large Action Models (PLAMs)," manage complex tasks on our behalf—negotiating services, handling purchases, and tailoring experiences to individual preferences. 🚸 AI Emotional Companions: AI "empathy" bots become commonplace for children and teens, providing companionship but raising concerns about their impact on emotional development and human relationships. 🕵️ Challenges with Trust and Reality: The proliferation of hyper-realistic AI-generated content blurs the lines between fact and fiction, potentially undermining societal trust and making it difficult to distinguish between real and fabricated information. 🦾 Autonomous Robots in Action: Advanced robots with enhanced autonomy and collaboration capabilities perform tasks in diverse environments—from industrial settings to homes—transforming industries like manufacturing, logistics, and healthcare. 💼 Workforce Transformation: AI reshapes occupations by automating certain tasks while creating new roles, affecting not only manual jobs but also impacting creative professionals, managers, and programmers. 📈 Economic Shifts and New Business Models: Companies that effectively integrate AI gain significant advantages, leading to productivity surges and the rise of new industry leaders, while others fail to adapt and decline. 🧠 Slow Progress Toward General AI: Despite rapid advancements, achieving artificial general intelligence remains elusive due to persistent issues like AI systems' tendency to make errors and "hallucinate," requiring genuine innovation and time. #AI2030 #FutureOfAI #ArtificialIntelligence #AIIntegration #HealthcareInnovation #PersonalAIAssistants #EmotionalAICompanions #TrustInAI #AutonomousRobots #WorkforceTransformation #EconomicShifts #AGIProgress 

Co-founder and CEO of Databricks Ali Ghodsi (rumored to be raising at a ~$61B valuation), has become one of the top minds in AI, evolving from researcher to leading one of the biggest AI-centered private companies. In our conversation, he broke down the AI hype cycle in detail and made bold predictions for the future. Here are a few highlights… → What people underestimate with AI (reading vs writing) There’s probably a 10:1 ratio on how often people are using AI to read vs write, and people underestimate how fast AI is at reading data compared to writing outputs. While we often focus on the output AI generates, its reading power offers enormous potential on its own. For example, AI is already being used in finance to analyze SEC filings and in healthcare to structure handwritten medical records. Beyond these, hundreds more untapped use cases across every industry have yet to be productized, and we’re still in the early stages of discovering where AI can automate the process of crunching information at scale. → Super AGI is still far off Ali thinks we’re going to see a slowdown in progress toward AGI. Over the past decade, we’ve had a 100 million times improvement in compute to throw at the problem, but the next few years are unlikely to sustain that pace. Ali would find the idea of cracking super AGI soon (requiring a recursive self-improvement loop) more likely if GPT-5 only took $1 & 1 second to make, or if progress were becoming cheaper and faster. Instead, we’re moving in the opposite direction, as the next models will be more expensive, involve more people, and require greater caution. Thus, Ali expects humans will remain essential for many years to ensure models are verifiable. A few more bull and bear takes: → We’re likely near the top of the hype cycle: Ali predicts a period of disillusionment in AI, where many companies will struggle or fail, and this will fuel a narrative of AI being overpromised. However, game-changing applications for humanity are likely to emerge afterward. → Compound AI systems will allow for faster improvement: Multiple components working together make it easier to improve AI output for specific tasks, as it’s easier to consistently debug and upgrade each part than to improve one gigantic, complex model. → Many startups will disrupt incumbents: Ali believes many startups (especially those with a data advantage) can disrupt slower-moving incumbents. → Most companies shouldn’t build their own chat apps: Many rushed to create internal AI tools (ex: for HR), but a single SaaS provider owning the workflow makes more sense. Ali sees this as the beginning of a growing trend in specialized AI companies. Many more perspectives in the full episode: https://lnkd.in/d4pmUvEB

As we step into 2024, I want to extend my warmest wishes to everyone in The Ravit Show Data & AI Community. As we toast to a new beginning, let’s look at 8 Key Trends in Data & AI -- 1. Semantic Layer: This year marks a significant leap in how machines interpret data. We're moving towards a semantic approach where data is not just numbers and text, but meaningful information that machines can understand contextually, and how we interact with AI systems. 2. Data Products: The concept of 'data as a product' is gaining momentum. It’s not just about collecting data anymore; it’s about refining it into a product that delivers real value - turning raw data into a strategic asset for better decision-making and customer insights. 3. Data Platforms: 2024 is seeing the evolution of data platforms into more sophisticated, integrated systems. These platforms are becoming the linchpin of our digital ecosystem, offering seamless access, processing, and analysis of data across various domains. 4. Multimodal Large Language Models (LLMs): LLMs are now going beyond text to understand and interpret multimedia content. This evolution opens up new avenues for AI applications in areas like content creation, media analysis, and interactive entertainment. 5. New Revenue Streams for Cloud Providers in Generative AI: Cloud computing is getting a major boost from generative AI. This symbiosis is creating novel revenue opportunities and transforming how we think about cloud services and AI capabilities. 6. Rise of Prompt Engineering: As AI becomes more prevalent, the art of prompt engineering is becoming critical. It's about effectively communicating with AI to generate precise and relevant outputs, a skill that's rapidly becoming essential in the tech workforce. 7. Data Privacy, Security, and Responsible AI Practices: With great power comes great responsibility. In 2024, there's an intensified focus on ethical AI, prioritizing data privacy and security. It's about building AI systems that are not only powerful but also trustworthy and responsible. 8. Metadata Management: 2024 is witnessing a surge in the importance of metadata in Data & AI. As we deal with ever-increasing volumes of data, managing metadata – the data about data – is becoming crucial. It’s not just about storing and accessing data anymore; it's about understanding its context, quality, and lineage. Effective metadata management leads to better data governance, quality, and usability, making it a pivotal aspect of data strategy in organizations. These trends are not just predictions; they are the pathways leading us to a more innovative and efficient future in Data & AI. What would you like to add? #data #datascience #datapredictions2024 #theravitshow