Forrester’s 2024 emerging technologies list highlights AI’s continued dominance and the increasing significance of security in a connected world. New technologies include generative AI, agentic AI, and advancements in security such as IoT security, Zero Trust edge, and quantum security. The future lies in investing in security now, given the expanding AI capabilities and potential vulnerabilities.
The evolution of the cloud has led to the adoption of cloud-native environments. Despite the high initial costs of cloud services, the benefits of flexibility, scalability, and future innovations outweigh on-premises options. Organizations moving to the cloud can experiment faster, innovate, and remain competitive.
AI risk management involves identifying and addressing potential risks associated with AI technologies to minimize negative impacts and maximize benefits. It is part of the broader field of AI governance, addressing data, model, operational, and ethical/legal risks. Frameworks like the NIST AI Risk Management Framework and ISO/IEC standards provide guidelines for responsible AI practices.
Around 42% of enterprises are using AI, with natural language processing chatbots being a popular application. However, challenges like inconsistent results and data relevancy remain. To address this, IBM has introduced retrieval-augmented generation (RAG) using platform-as-a-service (PaaS) solutions to improve generative AI outcomes. Real-world examples demonstrate its effectiveness and scalability.
Around a decade ago, Robotic Process Automation (RPA) garnered attention for automating repetitive tasks. However, RPA tools lack true intelligence. Organizations seek intelligent, adaptive systems to handle evolving data needs. Agentic AI systems, capable of pursuing complex goals with minimal supervision, show promise for the future of automation, surpassing current capabilities.
Metaphors are pervasive in everyday communication, with some claiming we use six metaphors per minute in conversation. Eco and Aristotle offer different insights on metaphors as cognitive instruments, and AI models’ capability to comprehend metaphors is evaluated in research papers. The “Metaphor Understanding Challenge Dataset for LLMs” demonstrates AI models’ difficulty understanding and paraphrasing metaphors.
Oracle recently solidified its cloud infrastructure with OpenAI and Google Cloud through partnerships. The collaboration with OpenAI extends Microsoft’s Azure AI to Oracle Cloud Infrastructure, while the agreement with Google Cloud aims to accelerate app modernization and migrations. This marks a significant move towards multicloud adoption driven by AI’s growing demand for capacity and may impact enterprise adoption of generative AI.
Cyber resilience and cybersecurity are two parts of the same protective strategy. Even the strong cybersecurity defenses won’t prevent all attacks, necessitating a plan for recovery. Key metrics like mean time to detect, acknowledge, contain, and resolve, along with security policy compliance rate and phishing attack success rate, gauge the effectiveness of cyber resilience.
A CPU, or central processing unit, serves as the computer’s brain, managing tasks and operational functions. Key components of CPUs include cache, clock speed, cores, and threads. They use a repeated command cycle to execute computing instructions. CPUs vary from single-core to deca-core processors. Major manufacturers include Intel and AMD. Future developments may involve new chip materials and quantum computing.
Hyperscalers, the dominant players in the cloud market, cater to enterprises with large computing needs. Amazon Web Services, Microsoft Azure, Google Cloud Platform, IBM Cloud, and Oracle are top providers, offering extensive solutions. Avoiding vendor lock-in and carefully managing costs are crucial for organizations considering hyperscaler adoption. Cloud portability across providers and private clouds is essential for a seamless experience.
Digital transformation is propelled by the merging of artificial intelligence and cloud computing, empowering businesses to innovate and boost efficiency. Prioritizing data strategies, aligning AI investments with business goals, fostering innovation, forming partnerships, and mindful cost management are key for businesses to stay competitive in the AI and cloud era.
Google’s Generative AI Overview for web searches has led to concerns for businesses, as AI-generated summaries appear at the top of search results, potentially reducing website traffic. Changes in Google’s search aim to de-rank sites that manipulate the search algorithm. Pierre DeBois and Duane Forrester discuss the impact and future business evolution. [Full episode link: https://www.informationweek.com/machine-learning-ai/navigating-google-s-ai-changes-to-search-is-seo-dead-%5D
AI, deep learning, and machine learning are intertwined technologies with distinct strengths and purposes. AI encompasses various techniques enabling machines to perform cognitive tasks, while machine learning focuses on learning from data. Deep learning uses neural networks to replicate human thought processes. Each has diverse applications, from automation and fraud detection to personalized marketing and predicting demand.
Generative AI, a transformative technology, relies on extensive, diverse, and well-managed datasets. Data’s pivotal role in shaping the success of generative AI initiatives cannot be overstated. Organizations must treat data as a strategic product, prioritizing quality, governance, and relevance. A value-driven approach and investment in data assets are essential for unlocking the full potential of generative AI.
