Titir Pal’s Post

Since 2011, productivity in the transport sector has declined by up to 25%. Here's how AI can help solve industry challenges. ⚡ Address operational inefficiencies 📚 Enhance human workflows 🚀 Drive productivity With only 1.3% of AI companies operating in the sector, discover how Digital Catapult offer valuable opportunities to accelerate adoption ➡️ https://lnkd.in/e_EDiPcR STFC Hartree Centre, The Alan Turing Institute, BSI, Innovate UK Business Connect  #transport #AI #innovation

Is AI Innovation Hitting a Wall? At the recent Cerebral Valley AI Summit, an important question emerged: has AI innovation hit a plateau? Despite access to vast data and advanced computing power, companies such as Google report diminishing returns. Are we on the brink of a new breakthrough, or are we encountering the limits of AI? This has sparked a vibrant debate: can emerging models surpass current technology, or have we reached the peak of AI capabilities? What are your views on the future trajectory of AI? Join the conversation and share your insights. #AIInnovation #TechTrends #FutureOfAI Learn more on website https://lnkd.in/eaEaieXg

Read our new blog post to learn how #AI is supercharging innovation across industries, including #automotive. "As auto companies strive for full self-driving capabilities, vehicles must be able to detect objects and navigate in real time. This requires high-speed data processing tasks, including feeding live data from cameras, lidar, radar and GPS into AI models that make navigation decisions to keep roads safe." The autonomous driving inference workflow is complex and includes multiple AI models along with necessary preprocessing and post-processing steps. Traditionally, these steps were handled on the client side using CPUs. However, this can lead to significant bottlenecks in processing speeds, which is an unacceptable drawback for an application where fast processing equates to safety."

I have always wondered about the changes AI has brought to the automobile industry. Curious, I started a new venture on how AI revolutionized this industry in the past years, to publish my first research paper. The topic I chose was "Emergence of Artificial Intelligence in Automobiles". The paper focuses on how Artificial intelligence has come about and made a deep impact on an industry which we thought would never require the usage and implementation of AI. Of course, being a 16-year-old I would not have been able to do this on my own. I would like to thank Bhawini Vasudeva for her continuous support and guidance throughout my venture. My paper can be read here: https://lnkd.in/drGjQtzB Do give it a read :) #AI #Automobile #Technology #Innovation

While the first wave in the AI adoption focused on individual efficiency, second wave to integrate AI into organizations, requires more than just cost-cutting measures. Companies must shift their focus from efficiency to productivity, harnessing AI for innovation and growth. Leveraging AI's potential demands R&D and the unique insights of employees, transforming it into a tool for unlocking latent expertise and driving true productivity gains. Check out the full article Latent Expertise: Everyone is in R&D - by Ethan Mollick.

AI research has produced huge breakthroughs in LLMs in the last couple years 👇 The emergence of large multi-modal models and the development of retrieval augmented generation (RAG) are prime examples of ongoing innovation. High-value experiences are already available for customers in nearly every industry, yet AI innovation continues to accelerate. And research mapping the journey forward. On July 10 at Generatvie AI Summit Austin, Shelby Heinecke, Senior AI Research Lead at Salesforce, will discuss two emerging directions in generative AI: Multi-agent systems  ➕ On-device AI. Join Shelby, and 250+ other forward-thinking AI practitioners from companies like Microsoft, Meta, Intel, IBM and more as they discuss the latest AI research and find solutions for the most common pitfalls in the industry. Download a brochure 👉 https://lnkd.in/eK-uciPs Checkout the agenda 👉 https://lnkd.in/ej4memxM Secure a pass 👉 https://lnkd.in/ectP7f9t #genai #austin #ai

Key Analysis around AI: Risk v. Reward "It is harder to put [GenAI] systems into practice, because they're unstable, and error prone... People are rushing to put these systems in." - Ran Balicer, HIMSS Board of Directors According to McKinsey's latest AI study, 80%+ of budget is still biased towards Machine Learning. Why? They're "simpler," have a decades long track record of stability and success, and requires far less computation and power needs than the new-to-the-neighborhood GenerativeAI models. In the field of healthcare, hallucinations from GenAI models like LLMs may have farther reaching detrimental effects than other industries as patients and healthcare workers are reliant on accurate information. There's also the risk of revealing certain Personal Identifying Information, and violating HIPAA. Its just as important to analyze when you shouldn't be leveraging a GenAI solution than when you want to. #healthcare #artificialintelligence #ai #machinelearning https://lnkd.in/gB4GMg9p

A new study from Cornell researchers has just revealed that increasing the number of AI agents collaborating on a problem can significantly improve performance. The researchers created many agents of an AI model and had them each work on a problem independently. The agents' answers were then combined using a voting system to determine the best overall solution. This study shows that there's a simple and effective way to make AI systems smarter: strength in numbers. As compute power and abilities continue to scale, automating unlimited amounts of agents to complete tasks could lead to mind-blowing increases in capabilities. The study found that increasing the number of agents improved accuracy across tasks using various LLMs like Llama and GPT. A smaller LLM could match or outperform a larger one by scaling up the number of agents, with a 13B parameter Llama model beating a 70B version on some tasks. Could this study pave the way for more effective and efficient AI systems? #AI #Automation #MachineLearning #TechNews

How Much Are Companies Investing in AI? According to our latest report, 11% of tech companies are allocating more than 20% of their digital budgets to AI. This investment covers both generative AI and analytical AI, with a well-balanced approach across various sectors. Curious about how your industry stacks up? Download the full report here! https://lnkd.in/dmpbKKgh

𝐄𝐯𝐞𝐫𝐲𝐨𝐧𝐞 𝐬𝐡𝐨𝐮𝐥𝐝 𝐤𝐧𝐨𝐰! As AI continues to evolve, staying updated with the latest terminology is crucial. Check out Susanna Ray’s article highlighting some key terms that are shaping the future of AI https://lnkd.in/dnHXP-uk #AI #ArtificialIntelligence #Microsoft #TechTrends #MachineLearning #Innovation