Earlier this year, the Stanford Institute for Human-Centered Artificial Intelligence released the 2024 AI Index Report, its seventh annual edition of the impact, progress, and trends in AI. At over 500 pages and 9 chapters, the report aims to be the world’s most authoritative source for data and insights about AI in order to help develop a more thorough understanding of the rapidly advancing field of AI.
Read MoreOur evolving digital world is getting trickier and trickier to protect. Every organization is now a target in the present digital environment, and every firm, big or little, has operations, a brand, a reputation, and revenue funnels that could be at significant danger from a breach.
Read More“Machine learning has an AI problem,” wrote author Eric Siegel in a recent Harvard Business Review (HBR) article, “The AI Hype Cycle is Distracting Companies.” “With new breathtaking capabilities from generative AI released every several months — and AI hype escalating at an even higher rate — it’s high time we differentiate most of today’s practical ML projects from those research advances. This begins by correctly naming such projects: Call them ML, not AI. “Including all ML initiatives under the AI umbrella oversells and misleads, contributing to a high failure rate for ML business deployments. For most ML projects, the term AI goes entirely too far — it alludes to human-level capabilities.”
Read MoreMachine learning is powering most of the recent advancements in AI, including computer vision, natural language processing, predictive analytics, autonomous systems, and a wide range of applications. Machine learning systems are core to enabling each of these seven patterns of AI. In order to move up the data value chain from the information level to the knowledge level, we need to apply machine learning that will enable systems to identify patterns in data and learn from those patterns to apply to new, never before seen data. Machine learning is not all of AI, but it is a big part of it.
Read MoreAI in healthcare now involves the use of machine learning algorithms and patient data fed in timely ways to these algorithms. IoT is making its way into hospital devices and equipment and is sending this data to computing facilities for AI to work its valuable guidance on it. This consistent data flow streamlines the patient experience by producing inferences from multiple data points, which help to guide and improve healthcare management capabilities with information and data while handling patients, equipment, and procedures.
Read MoreThe pace of adoption for AI and machine learning continues unabated with global, widespread, adoption and usage. It’s not just companies that are taking note of the tremendous value AI can provide them. Countries and governments around the world are also seeking competitive advantages by harnessing the power of AI. Governments that can take advantage of the tremendous transformation presented by AI and cognitive technologies can position themselves for global competitiveness in the future. As a result, countries around the world are adopting AI strategies to provide roadmaps, funding, education, and strategies needed to differentiate themselves and become leaders in different areas related to AI and cognitive technology.
Read MoreWhether your organization has an extensive cybersecurity initiative in the event of a crisis such as this pandemic or not, there are things that you can do now that your employees are working remotely.
You can use this opportunity to increase cybersecurity awareness across your organization.
Read MoreJust think for a moment about how much online searching you do. Need to find a nearby Thai restaurant? Just type your query into the search engine and presto: You receive page after page of results listing eateries in your area offering Pad Thai. Need to know the forecast in Austin? Again, punch in your query and you will receive no shortage of pages offering three-day forecasts, five-days forecasts, even year-round averages.
Read MoreArtificial intelligence feeds on data, and data is piling up from increasingly cheap sensors and surging Internet use: videos, images, text; time series data, machine data; structured, unstructured and semi-structured data. And while AI is currently confined to narrow problems in discreet domains, the ambition of machine-learning researchers globally is to write algorithms that can cross domains, transferring learning from one kind of data to another.
Read MoreLast year was a transformative year for technological innovation. The threats from Covid19 upended our way of living, especially related to remote-work and pushed forward the timetable of digital transformation. As we move ahead into another year of unprecedented technology advancement, it is useful to examine some of the trends, & technologies that are already shaping 2021 and what may be on the future horizon.
Read MoreMany humans enjoy Artificial Intelligence personally at home – ranging from just-for-you movie recommendations on Netflix to product recommendations on Amazon. On the contrary, most humans don’t use AI professionally at work. AI-based recommendations are powerful and personalized for you, so why hasn’t it become an everyday phenomenon at work? What if, with your permission and oversight, AI could automatically do your repetitive tasks? What if AI suggested the answer for your next business email reply? What if AI could suggest solutions for your complex work problems based on decades of data from billions of humans and projects?
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