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) …
What do Google Assistant, Siri, Alexa, and Cortana have in common? They tell jokes of varying cleverness, most of which are the work of writing teams operating behind the scenes. They're entertaining, but preliminary research suggests they also play a part in making interactions with assistants engaging. In pursuit of assistants capable of tailoring jokes to individual users' tastes, Amazon researchers investigated joke selection methods that tap either a basic natural language processing model or a machine learning model. They say that when tested against production data, both approaches "positively" impacted user satisfaction and potentially improved joke-telling.
A new machine-learning model can predict response to hypomethylating agents in patients with myelodysplastic syndromes, according to new research presented at the American Society of Hematology (ASH) 2019 Annual Meeting in Orlando. Investigators from the Cleveland Clinic developed a clinical artificial intelligence (AI) model to predict response and resistance to hypomethylating agents after 90 days of initiating therapy. The model is based on changes in blood counts using time series analysis technology. The team trained the AI using absolute values and changes in complete blood count values. Nathan Radakovich, MD, from the Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, and his team screened 107 patients with myelodysplastic syndromes who received hypomethylating agents at the Cleveland Clinic from 2005–2013.
Artificial Intelligence, Machine Learning and High Velocity Analytic workloads are going mainstream. Enterprises of all types and sizes want to seize the opportunity their data presents. As these workloads move from development to production, organizations face a significant challenge with the supporting storage architecture. At the heart of the problem is the file system the organization will use to store the information. It needs to be fast, scalable, durable and cloud-ready.
In my last blog "All AI Paths Lead to the Cloud," I talked about how the FP&A challenges facing finance leaders are not going away. These issues are only compounding due to an overabundance of data and the rapid evolution of hyper-connected mobile employees, driving businesses towards the availability, scalability, and affordability that comes with putting their financial applications to the cloud. The era of simply throwing more people and resources at the challenges simply does not economically scale. Well let's think about the problem: With more people comes additional costs (headcount, manual errors, delays, etc.) For some companies this has become the status quo, meaning they are willing to assume a certain risk tolerance that results in the under-utilization of highly skilled, well-paid assets.
SoftBank Group Corp. founder Masayoshi Son unveiled a $184 million initiative Friday to accelerate artificial intelligence research in Japan, enlisting Alibaba's Jack Ma to expound on his goal of commercializing the technology. Son's company announced a partnership with the University of Tokyo that includes spending 20 billion yen ($184 million) over 10 years by mobile arm SoftBank Corp. to establish the Beyond AI Institute. He roped in the Alibaba Group Holding Ltd. co-founder for an on-campus chat, during which the two billionaires discussed their vision for the future of technology. The institute will support 150 researchers from various disciplines and focus on transitioning AI research from the academic to the commercial using joint ventures between universities and companies. Health-care, city and social infrastructure and manufacturing will be the primary areas of focus, SoftBank Corp. said in a statement.
As we approach the "visionary" year of 2020, we took a look at what the New Year has in store for the Digital Advertising industry. Here are key things to watch out for as you plan ahead and finalize your Marketing budgets. Brands have begun to understand the power of advertising on Amazon and the unique opportunity it offers to capture people at the beginning of their purchasing journey. The Opportunity: Brands have flocked to Amazon for its revenue-generating ad capabilities. We expect this trend to continue in 2020 as Amazon refines its offering and advertiser use becomes more sophisticated.
AI for Longevity has more potential to increase healthy Longevity in the short term than any other sector. The application of AI for Longevity will bring the greatest real-world benefits and will be the main driver of progress in the widespread extension of healthy Longevity. The global spending power of people aged 60 and over is anticipated to reach $15 trillion annually by 2020. The Longevity industry will dwarf all other industries in both size and market capitalization, reshape the global financial system, and disrupt the business models of pension funds, insurance companies, investment banks, and entire national economies. Longevity has become a recurring topic in analytical reports from leading financial institutions such as CitiBank, UBS Group, Julius Baer, and Barclays.
Background: The current gold standard for measuring sleep is polysomnography (PSG), but it can be obtrusive and costly. Actigraphy is a relatively low-cost and unobtrusive alternative to PSG. Of particular interest in measuring sleep from actigraphy is prediction of sleep-wake states. Current literature on prediction of sleep-wake states from actigraphy consists of methods that use population data, which we call generalized models. However, accounting for variability of sleep patterns across individuals calls for personalized models of sleep-wake states prediction that could be potentially better suited to individual-level data and yield more accurate estimation of sleep.
Run the following command to create training instances. Download pre-trained knowledge embedding from Google Drive/Tsinghua Cloud and extract it. Download pre-trained ERNIE from Google Drive/Tsinghua Cloud and extract it. Note that the extraction may be not completed in Windows. As most datasets except FewRel don't have entity annotations, we use TAGME to extract the entity mentions in the sentences and link them to their corresponding entitoes in KGs.
Sign in to report inappropriate content. SharkEye is a research effort that uses Artificial Intelligence (AI) to detect great white sharks to learn more about their biology and help people safely share the ocean with marine wildlife. It is a collaborative effort by UC Santa Barbara's Benioff Ocean Initiative and the Salesforce AI Research team.