One of the most promising technologies in the past few years is composite AI. Companies will have an unmatched competitive edge now and in the future if they fully leverage the benefits of composite AI. It would be difficult finding an enterprise that isn't utilizing AI in 2022. In recent years, AI has expanded in popularity and accessibility, solidifying its position as a key component in the technological lexicon. Composite AI is something that is not yet immensely popular but is becoming increasingly important for successful AI deployments.

Artificial Intelligence (AI) is no longer the new kid on the block regarding the hottest enterprise technologies. It's been around for many years and has been deployed for numerous use cases in many different sectors, including Financial Services (FS). AI can be transformative for banks. It helps locate and manage data, providing actionable insights that allow banks to manage and mitigate risks more effectively, better understand customer requirements, improve the sales pipeline and much more. While it remains effective, certain limitations have emerged.

AI Researcher, Cognitive Technologist Inventor - AI Thinking, Think Chain Innovator - AIOT, XAI, Autonomous Cars, IIOT Founder Fisheyebox Spatial Computing Savant, Transformative Leader, Industry X.0 Practitioner What are the current AI or machine learning research trends? NLP AI, large neural networks trained for language understanding and generation, the best shortcuts to artificial general intelligence. Large language models, such as PaLM, GLaM, GPT-3, Megatron-Turing NLG, Gopher, Chinchilla, LaMDA, are led by WuDao 2.0 model trained by studying 1.2TB of text and 4.9TB of images using 1.75tn parameters to simulate conversations, understand pictures, write poems and create recipes. It all is relying on unlimited brute force scaling, tens of gigabytes in size and trained on enormous amounts of text data, sometimes at the petabyte scale. The Pathways Language Model (PaLM), a 540-billion parameter, dense decoder-only Transformer model trained with the Pathways system, which enabled us to efficiently train a single model across multiple TPU v4 Pods.

Neuro-Symbolic AI, which is alternatively called composite AI, is a relatively new term for a well-established concept with enormous significance for almost any enterprise application of Artificial Intelligence. By combining AI's statistical foundation (exemplified by machine learning) with its knowledge foundation (exemplified by knowledge graphs and rules), organizations get the most effective cognitive analytics results with the least amount of headaches--and cost. Pairing these two historical pillars of AI is essential to maximizing investments in these technologies and in data themselves. By itself, rules-based symbolic reasoning doesn't improve over time. Together, these AI approaches create total machine intelligence with logic-based systems that get better with each application.

The pandemic time impacted many aspects of both our private and professional lives. Without any doubt we all faced significant challenges while required immediately to move fully into remote working. We stopped working from the office, meeting our colleagues and clients face to face and stopped travelling. That sudden shift changed not only the way we work, but also changed all our daily activities both business and private ones and significantly reduced our social interactions in the real-world, impacting also our mental sphere. And probably not everyone was taking enough care about the right work-life-balance and activities to keep both physical and mental health.

SAS brings multiple technologies and prebuilt applications to the composite AI table – machine learning, statistics, data mining, forecasting, optimization, NLP, computer vision, etc. – providing them all under a single platform, making SAS unique in the AI and analytics space.

"As soon as it works, nobody calls it AI anymore." Those were the words of John McCarthy, a computer scientist who is considered one of the founding fathers of artificial intelligence. It makes you wonder when artificial intelligence (AI) will stop being a disruptive technology and just become something everyone uses to do things more efficiently. One way to gauge the maturity of any given technology is to see where it sits on the Gartner Hype Cycle. As it turns out, artificial intelligence has spawned its own Gartner Hype Cycle.

Chatbots are projected to see over a 100% increase in their adoption rates in the next two to five years and are the leading AI use cases in enterprises today. Gartner revised the bots' penetration rate from a range of 5% to 20% last year to 20% to 50% this year. Gartner points to chatbot's successful adoption as the face of AI today and the technology's contributions to streamlining automated, touchless customer interactions aimed at keeping customers and employees safe. Bot vendors to watch include Amazon Web Services (AWS), Cognigy, Google, IBM, Microsoft, NTT DOCOMO, Oracle, Rasa and Rulai. GPU Accelerators are the nearest-term technology to mainstream adoption and are predicted to deliver a high level of benefit according to Gartner's' Priority Matrix for AI, 2020.

Despite the global impact of COVID-19, 47% of artificial intelligence (AI) investments were unchanged since the start of the pandemic and 30% of organizations actually planned to increase such investments, according to a Gartner poll. Only 16% had temporarily suspended AI investments, and just 7% had decreased them. During the pandemic, for example, AI came to the rescue. Chatbots helped answer the flood of pandemic-related questions, computer vision helped maintain social distancing and machine learning (ML) models were indispensable for modeling the effects of reopening economies. "If AI as a general concept was positioned on this year's Gartner Hype Cycle, it would be rolling off the Peak of Inflated Expectations. By that we mean that AI is starting to deliver on its potential and its benefits for businesses are becoming a reality," says Svetlana Sicular, VP Analyst, Gartner.

Health passports are mobile apps that indicate the relative level of infection risk a person is and whether they can gain access to buildings, supermarkets, restaurants, public spaces and transportation. Early adopters in China and India are proving the combination of health passports and screening methodologies that are effective in stopping the spread of Covid-19 while also giving people the freedom to use public spaces and transportation. China's Health Code is widely used as a screening tool to minimize the risk of Covid-19 transmission. It provides the user with a color QR code based on their designated health status: Red is confirmed infected with Covid-19; Yellow should be in quarantine and Green is free to travel. Health Code checks are very common, making it difficult to move without having a green code.