If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
IBM this week revealed it has added a drift detection capability to the Watson OpenScale platform to govern artificial intelligence (AI) models that will become a foundational piece of IBM's approach to defining best DevOps practices for building AI-infused applications. Rohan Vaidyanathan, program director for IBM Watson OpenScale, said one of the biggest AI issues organizations face today is determining when to update or replace an AI model. Announced at the IBM Data and AI Forum event, the drift detection software added to Watson OpenScale provides a continuous monitoring capability that detects how far an AI model has moved from its original parameters, Vaidyanathan said. Drift in AI models usually occurs over time, especially as use cases change in ways that are unexpected. Once that drift is detected, organizations eventually want to either retrain that AI model or replace it with a new one.
The high-tech industry has been a leading adopter of artificial intelligence (AI), so much so that it influences the digital transformation trends in most other industries. Technology giants are not just building AI solutions, they are acquiring smaller AI companies to build more capabilities and finding new use cases outside the IT department, in our offices, hospitals, and homes. Yet, as per leading research reports these firms are still lagging in the overall AI adoption when compared with other digitization efforts. This paper attempts to find the factors behind the high-tech industry's failure to scale and the best practices the industry can adopt to encourage mainstream adoption. It has been almost seven decades since Alan Turing first envisioned a'thinking' machine that could potentially carry on a conversation with a human that was indistinguishable from a human-to-human interaction.
The automobile is a transformative invention that changed the technological, social, and economic fabric of our society. Now, we are witnessing a new era of accelerated automotive disruption, which according to McKinsey is likely to be far more impactful than the previous 50 years. While industry analysts are not entirely clear what the future of automotive technology holds, we know that SAP customers in the industry are focused on three central trends: driving the business with artificial intelligence, applying automation everywhere, and moving to autonomous vehicles. These trends are putting pressure on automotive businesses to make their operations fully digital, organize and manage their data more effectively, and integrate and connect their critical business systems.
Report explores how organizations using AI are making it work, what technologies they're using, and what best practices can maximize an organization's success with AI and machine learning Seattle, WA, Oct. 18, 2019 (GLOBE NEWSWIRE) -- TDWI Research has released its newest Best Practices Report, Driving Digital Transformation using AI and Machine Learning. This original, survey-based report looks at the many dimensions of artificial intelligence (AI) and machine learning (ML) so data professionals and their business counterparts can understand the benefits of the technology, how it's used (and by whom), and how enterprises are achieving success with it. The author of the report, Fern Halper, is vice president and senior director of TDWI Research for advanced analytics. She explains that organizations are embracing AI and ML to gain better insights, make better decisions, and improve competitive advantage. "In fact," she writes, "AI is at the heart of the digital revolution around analytics occurring today. AI promises to help organizations improve their operations and processes and to drive new revenue opportunities."
Deriving scientific insights from artificial intelligence methods requires adhering to best practices and moving beyond off-the-shelf approaches. Artificial intelligence (AI) methods have emerged as useful tools in many Earth science domains (e.g., climate models, weather prediction, hydrology, space weather, and solid Earth). AI methods are being used for tasks of prediction, anomaly detection, event classification, and onboard decision-making on satellites, and they could potentially provide high-speed alternatives for representing subgrid processes in climate models [Rasp et al., 2018; Brenowitz and Bretherton, 2019]. Although the use of AI methods has spiked dramatically in recent years, we caution that their use in Earth science should be approached with vigilance and accompanied by the development of best practices for their use. Without best practices, inappropriate use of these methods might lead to "bad science," which could create a general backlash in the Earth science community against the use of AI methods.
Organizations have been striving to increase productivity since the days of the cotton gin, steam power and Model T assembly lines. Today, maximizing productivity is often associated with software technology, from virtual assistants to predictive science. But, as the pace of innovation has accelerated, the practical ability to implement and monetize the exciting new technologies hasn't always kept up. It's time for solution providers to step up their game and take a more active role in supporting software implementation efforts. Today's common tactics for deploying software are flawed.
Practical adoption of artificial intelligence (AI) faces a variety of roadblocks--splashy, high-profile deployments of AI have not been received well, with Microsoft's "Tay" bot on Twitter parroting anti-Semetic vitriol just 16 hours after launch. Similarly, Amazon's AI-powered hiring process displayed bias against women and the company marketed unreliable facial recognition technology to municipal law enforcement. AI often reflects the biases--including, and especially, unconscious biases--of the designers, which would make Facebook attempting to build an AI with an "ethical compass" a concerning prospect, given the multitude of other problems the social network has experienced. This is a problem that necessarily requires diversity of thought, according to Northeastern University's Ethics Institute and professional services firm Accenture, which published a guide to building data and AI ethics committees. Such committees are, by definition, not achievable by pooling together people of similar backgrounds to debate the merits of AI design.
Think you are interested in speaking in our amazing DevFest event? Google Developer Group DevFests is the largest Google-related events in the world! Each DevFest is carefully crafted for you by your local GDG community to bring in awesome speakers from all over the world, great topics, like Machine Learning, AI, Flutter, AR core, VR, Kotlin, AMP Html, and lots of fun! Demos - 5 min presentation - an interactive session where the audience gets the chance to show more about their products, apps and innovative idea. You can submit as many talks as you want. We support the diversity of our speakers and welcome underrepresented groups in tech such as women.
Summa Health is a nonprofit health system in Northeast Ohio. The Greater Akron Chamber documents Summa Health as the largest employer in Summit County with more than 7,000 employees. Summa Health provides comprehensive emergency, acute, critical, outpatient and long-term/home care. The growing use of CT chest imaging has resulted in increased incidental lung nodule findings on imaging studies. These nodules have historically proved problematic for follow-up.
THIS EVENT IS 100% FREE! Industry 4.0 and Smart Manufacturing – these are the buzzwords that are floating around manufacturing facilities across the United States. What does this mean for you? How are you gaining knowledge about these new technologies? Are you keeping up with your competitors who are being innovative and implementing smart manufacturing in their facilities?