While organizations around the globe have long gone on an AI investment spree, the number of artificial intelligence projects that make it from prototypes to production still fluctuates around 53%. Experts believe this often happens due to lacking tech skills, human resources, and tools to scale isolated AI proof of concepts (PoCs) across other use cases. Foundation models – i.e., large machine learning models trained on vast volumes of unlabelled data under the guidance of skilled AI consultants – may be the ultimate answer to the daunting AI scalability and cost problems. Your company could use such models as a starting point to enhance or automate various tasks, from converting paper-based documents into editable text files to uncovering customer sentiment in social media reviews. And build on your AI excellence from there, adapting foundation models for future tasks and use cases. This language model has absorbed tremendous volumes of conversational text using the supervised learning and, at the fine-tuning stage, the reinforcement learning from human feedback (RLHF) approaches.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - August 3. Join AI and data leaders for insightful talks and exciting networking opportunities. In a previous post, I described how to make sure that marketers minimize bias when using AI. When bias sneaks in, it will significantly impact efficiency and ROAS. Hence, it's critical for marketers to develop concrete steps to ensure minimal bias in the algorithms we use, whether it's your own AI or AI solutions from third-party vendors. In this post, we're going to take the next step and document the specific questions to ask any AI vendor to make sure they're minimizing bias.

The latest research study released by HTF MI evaluating the market risk side analysis, highlighting opportunities and leveraged with strategic and tactical decision-making support. The market Study is segmented by key a region that is accelerating the marketization. The oil and gas (O&G) industry faces many severe challenges. The shortage of easily accessible hydrocarbon reserves forces companies to use remote reserves that are hard to discover, costly, and risky. Moreover, sustainability concerns are shifting demand away from O&G toward cleaner sources, and COVID-19 has further suppressed the demand.

There is fair amount of excitement and hype about the ongoing emergence of artificial intelligence (AI) and the potential promise of the technology in improving diagnostic accuracy and increasing workflow efficiencies in radiology. However, as Nina Kottler, MD, points out in a recent video interview with Diagnostic Imaging, there are challenges as well when it comes to assessment and implementation of AI into one's practice. While there are "hundreds of FDA-cleared and approved algorithms in radiology alone," she notes that it is "early on in the maturity of the technology of AI with respect to health care" so choosing the right AI vendor for your practice is critical. While technical prowess is important, 80 percent of what an AI vendor does is help create the workflow around the algorithm to ensure it works well, according to Dr. Kottler, the Associate Chief Medical Officer for Clinical AI and VP of Clinical Operations at Radiology Partners. Dr. Kottler says cultural alignment is an important consideration as you are seeking a vendor that values your input as a radiologist and is on a similar wavelength with you on future directions of AI in radiology. For pertinent insights on the assessment and implementation of AI technology, watch the video below.

All the sessions from Transform 2021 are available on-demand now. Most AI vendors develop solutions that target broad use cases with large markets. This is because investors have shown they are only interested in a target market if it is worth several billion dollars. Therefore smaller markets have been excluded, and AI solution ideas designed for niche markets often die out and the companies behind them come to a standstill before they have the chance to see the light of day. Another side effect of the limited capital to build niche models is that AI vendors tend to build one model and market it to a large set of disparate users.

Merger and acquisition activity among analytics vendors has picked up in recent months. After more than a year between Salesforce's $15.7 billion acquisition of Tableau in June 2010 and Tibco's purchase of IBI (formerly Information Builders) for an undisclosed amount in October 2020, recent months have been a busy time for merger and acquisition activity. Talend was acquired by private equity firm Thoma Bravo in March 2021. Logi Analytics was acquired by ERP vendor Insightsoftware in April 2021 and subsequently purchased Izenda just eight days later. And ThoughtSpot, after going nine years without acquiring another company, has acquired two so far in 2021.

The adoption of AI in health care is being driven by an exponential growth of health data, the broad availability of computational power, and foundational advances in machine learning techniques. AI has already demonstrated the potential to create value by reducing costs, expanding access, and improving quality. But in order for AI to realize its transformative potential at scale, its proponents need business models optimized to best capture that value. AI changes the rules of business and, as ever, there are some unique considerations in health care. In order to understand these, we studied AI across 15 sets of use cases. These span five domains of health care (patient engagement, care delivery, population health, R&D, and administration) and cover three types of functions (measure, decide, and execute).

The insurance industry is a competitive sector representing an estimated $507 billion or 2.7 percent of the US Gross Domestic Product. As customers become increasingly selective about tailoring their insurance purchases to their unique needs, leading insurers are exploring how machine learning (ML) can improve business operations and customer satisfaction. The greatest opportunities seem to lie, perhaps unsurprisingly, in claims and underwriting. No other sources have taken a comprehensive look at the impact of AI among the leading insurance companies in the U.S. We researched this sector in depth to help answer questions business leaders are asking today: This article aims to present a comprehensive look at the four leading insurance companies and their use of AI. Our "top 4" rankings are based on the National Association of Insurance Commissioners' 2016 ranking of the top 25 insurance companies.

As AI use and innovation continue to increase globally, getting small and medium-size AI companies more involved in the financially lucrative and still-nascent AI revolution is a developing goal of the US government. One federal agency, the National Security Commission on Artificial Intelligence (NSCAI), is asking smaller AI vendors to send in comments and suggestions by Oct. 23 about how to make it easier for them to work with the US government to bolster commercial AI innovation. With the comment deadline looming, the NSCAI says it seeks detailed input from small- and medium-sized AI vendors, particularly when it comes to working together to catalyze AI development, expand the national security innovation base, and make it easier for them to do business with the federal government. The agency says it is specifically seeking recommendations involving needed statutes, regulations, policies, budgets, organizations, and cultures, as well as other related issues. The NSCAI's appeal for comments and input from smaller AI vendors was published Sept. 23 in The Federal Register.

Most companies that adopt AI into their workforce follow a similar pattern of implementation. They think of a perfect proof of concept and partner with an AI vendor that promises to launch this system for them. Time, money and effort are expended in ample amounts to ensure that the project succeeds. Even after all this processing, many implementations hit a dead end. This failure occurs because AI integration into an already working system is an immensely difficult task. To do so requires not only a top-notch AI system, but also a good connection with the existing system.