AI diagnostics startup MediCircle Health has recently introduced in India a rapid spectrometry-based test that employs machine learning and artificial intelligence to detect COVID-19. Spectral Instant Test (SpectraLIT) is a point-of-care diagnostic platform that performs spectral analysis to accurately and instantly determine if a spectral pattern of a virus from a nasal or mouthwash sample resembles SARS-CoV-2, the virus causing COVID-19. The test can deliver results "within seconds of its use", according to a press release by MediCircle. The company shared that the portable solution can be used for entry screening at various airports, malls, schools and other venues. It can also potentially enable secure and real-time reporting to health and other designated authorities.

And yet rapid tests like the Abbott test have led to reports among the general population of false negatives (reports that you don't have the virus when you really do). That means some people may have been unknowingly spreading the virus to others. The White House outbreak is a very good illustration of the limitations of rapid testing. But it should not deter us from the strategy entirely--we just need to use the technology properly. No test is 100% accurate, but the gold standard for diagnosing covid-19 is a PCR test.

In order to compete in the new digital economy, businesses must become increasingly data-driven. Few executives would dispute this objective. Recent events, including the global outbreak of COVID-19, have underscored the critical importance of having reliable data to inform organizational decision-making. Yet companies continue to struggle to operate in a data-driven manner. Even though we are now decades into the age of competing with data, a 2020 NewVantage Partners survey of C-suite executives representing more than 70 Fortune 1000 companies found that only 37.8% of companies have created a data-driven organization.

These and many other insights are from Capgemini Research Institute's research of enterprises' AI pilot, production and spending during the pandemic provide insights into what differentiates AI-at-scale leaders versus organizations struggling to get results. The AI-powered enterprise: Unlocking the potential of AI at scale is based on 950 interviews with AI leaders in enterprises with annual revenues between $1B to $25B, distributed across 11 nations. The study was completed in March and April 2020. For additional details on the methodology, please see pages 31 and 32. Capgemini has made the report available for free download here (PDF, 40 pp, no opt-in).

TORONTO, ONTARIO – According to a new research report by the market research and strategy consulting firm, Global Market Insights, Inc, penetration of AI technologies in the retail market will exceed 8 billion USD by 2024. The AI in retail market is driven by increasing investments across the globe. This growing interest can be attributed to the wide applications of machine learning, predictive analytics, and deep learning. Furthermore, AI is set to unleash the next phase of the digital disruption in retail – and the major players in the sector are ramping up their digitalization efforts as a result. With a storied history in the AI and data analytics space, the founder and CEO of Daisy Intelligence, Gary Saarenvirta, is today working at the forefront of the disruption taking place globally in retail.

In a crowded, rapidly developing field like artificial intelligence, decision makers in enterprises can struggle when deciding where to invest their company's resources. Almost every emerging company providing data science services will tout their AI expertise, but which vendors actually have the proficiency required to deliver valuable change and which are only claiming they have AI capabilities to capitalize on the buzzword?

Artificial intelligence is starting to play a transformational role in the healthcare industry, even if opportunities for using it are just beginning to be explored. The Department of Health and Human Services and the Robert Wood Johnson Foundation commissioned the report; the names of the scientists who developed the report are not being released. Computers can match human competence in image recognition and, in some studies, can make diagnostic decisions on medical images that match or exceed the ability of clinicians. Technology is also getting better at speech recognition and natural language processing. However, while the healthcare industry has a huge amount of data, the quality and accessibility to pertinent data at an affordable cost remains a challenge, as does the protection and sharing of data.

Machine Learning is a complex area of computing for those with deep technical knowledge and the ability to translate between business requirements, large data sets and how computing systems develop. At least, that is how it has been since first discussed in 1959, and continued until today. Over the next few years, it's been predicted that Machine Learning will become business as usual. Initially, companies will use machine learning to automate certain functions, such as pattern recognition, and improve efficiencies. Over time, Machine Learning will expand to automate more of the analytics steps involved within jobs.