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) …
AI software company C3.ai (NYSE:AI) and software services firm CITI have been awarded a joint $90M, five-year contract from the U.S. Department of Health and Human Services (HHS). The blanket purchase agreement will expedite enterprise AI deployments across the entire HHS. The agreement enables various HHS agencies and missions to select C3 AI (AI) solutions to support data-driven work. Specifically, it enables HHS officials to procure C3 AI applications, C3 AI's AI platform for data collection, analysis with machine learning, and predictive AI capabilities and CITI services. The BPA allows HHS officials to procure CITI services, C3 AI applications, and C3 AI's secure and powerful AI platform for data collection, analysis with machine learning, and predictive AI capabilities.
Welcome back to the last article in our Lean Machine Learning series. Before beginning this article, we recommend you check out Part 1 and Part 2 first. As a quick recap, we've been discussing how to build better machine learning products by applying Eric Ries' Lean Startup approach. In Parts 1 and 2, we described how to make a desirable, feasible, and viable machine learning product. Each of these areas is part of the traditional Innovation Sweet Spot (ISS). We've dedicated our final article in this series to an extremely important topic -- Ethics.
There has been a large number of courses that teach the fundamentals of programming and data science. They do a good job in reinforcing various concepts in machine learning and show various steps that are usually followed when building a project with ML capabilities. While these courses mostly focus on the theoretical aspects of machine learning, it can be handy if one learns to put more emphasis on the good practices when building applications related to data science and machine learning. With the rise in data and an exponential increase in the compute power, there has been a rapid increase in the demand for people who would make use of the data and generate predictions along with useful insights depending on the use case of the project. Furthermore, there are numerous data related positions such as data engineer, data architect, data scientists, deep learning engineer and machine learning engineer.
We often hear about the benefits artificial intelligence (AI) can bring to medicine and healthcare through drug research, but could it also pose a threat? Researchers from Collaborations Pharmaceuticals, a North Carolina-based drug discovery company, have published a paper that highlights the dangerous potential of AI and machine learning to discover biochemical weapons. By simply tweaking a machine learning model called MegaSyn to reward instead of penalise predicted toxicity, their AI was able to generate 40,000 biochemical weapons in six hours. Worryingly, the researchers admitted to never having considered the risks of misuse involved in designing molecules. "The thought had never previously struck us. We were vaguely aware of security concerns around work with pathogens or toxic chemicals, but that did not relate to us; we primarily operate in a virtual setting. Our work is rooted in building machine learning models for therapeutic and toxic targets to better assist in the design of new molecules for drug discovery. We have spent decades using computers and AI to improve human health--not to degrade it," the paper noted.
Attaining optimum manufacturing outcomes requires a high amount of precision in multiple areas. A typical manufacturing facility is often a complex mix in a heterogeneous environment of physical assets and systems from different vendors, varying workforce skill sets and experience levels, complex supply chains, and more. Interconnectivity problems between myriad software systems and controllers can make it challenging to gain actionable and timely insights from the flood of data that is generated on a typical plant floor. A key enabler for production optimization requires implementing versatile data-driven solutions that can be easily customized for multiple personas – from the production floor worker to the executive boardroom. Hitachi and Amazon Web Services have partnered to solve these challenges by quickly producing insights around machine status, production, quality, asset management and supply chains using AI-powered manufacturing solutions that run on AWS-native services.
Bob van Luijt is CEO of SeMI Technologies the company behind the open-source vector search engine Weaviate. A new ecosystem of smaller companies is ushering in a "third wave" of AI-first database technology. New search engines and databases brilliantly answer queries posed in natural language, but their machine-learning models are not limited to text searches. The same approach can also be used to search anything from images to DNA. Much of the software involved is open source, so it functions transparently and users can customize it to meet their specific needs.
Outward Media, Inc. (OMI), a leading provider of multi-channel marketing data, announced it has partnered with Snowflake, the Data Cloud company, to launch its high-quality B2B contact data on Snowflake Marketplace, a centralized platform where customers can securely access live, ready-to-query data to unlock insights with just a few clicks. New sample data sets from OMI are now available on Snowflake Marketplace, along with data from many other third-party data providers and data service providers. With OMI data, Snowflake Marketplace customers can choose from a wide range of decision-maker titles--from manager-level to CEO--across industries. After testing the sample files, joint customers can work directly with OMI to license complete data sets, leveraging firmographics or digital intent signals to build custom audiences that meet their precise marketing needs. In addition, Snowflake customers can take advantage of OMI data on the Snowflake Media Data Cloud to dynamically share, join and analyze collaborative data for identity, audience insights, targeting, activation and measurement.
You are right that randomness will play a role (like with many other algorithms including MCMC samplers for Bayesian models, XGBoost, LightGBM, neural networks etc.) in the results. The obvious way to minimize randomness in the results of any hyper-parameter optimization method for RF (whether it's random grid-search, grid search or some Bayesian hyperparameter optimization method) is to increase the number of trees (which reduces the randomness in the model behavior - albeit at the cost of an increased training time). Alternatively, you construct a surrogate model on top of the results that takes into account that the signal, of where the best model in the hyperparameter landscape is, is noisy through an appropriate amount of smoothing/regularization.
The application of AI technologies has promoted a revolution in the business industry. When it comes to the public relations industry, AI applications also change the original workflow. Nowadays, to become a good PR participant, an employee is not only required to have practiced communications skills but also the ability to collaborate with AI-based platforms. This article will show you the new vision of public relations, exploring the 5 ways AI is changing the industry. With the digital transformation of business, social media now has become a critical battlefield for business operations.
Ever spend much too long trying -- and failing -- to rediscover articles you've partially read? This reporter's been there, and it seems I'm not the only one. According to 2021 Carnegie Mellon study on browser tab usage, many participants admitted to feeling overwhelmed by the amount of tabs they kept open but were compelled not to close them out of fear of missing out on valuable information. Samiur Rahman is familiar with the feeling -- so much so that he co-created a product, Heyday, to alleviate it. Launched in 2021, Heyday is designed to automatically save web pages and pull in content from cloud apps, resurfacing the content alongside search engine results and curating it into a knowledge base. Investors include Spark Capital, which led a $6.5 million seed round in the company that closed today.