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"The AI Collective Word: Empowering Business Leaders with the Insights to Shape the Future"

Author

Anne Borsohalmi
June 03, 2023

Welcome to the first edition of The AI Collective Word!

Here’s a recap of key headlines that happened in the Artificial Intelligence industry from Tuesday, May 30, to Friday, June 2, 2023.

TRENDING NEWS

INDUSTRY TRENDS

Chief information officers could see their job getting more complex even as generative AI makes software development easier.

In 2019, I wrote a post on how companies should price their AI-enabled software. I focused on SaaS companies that were developing their own AI and highlighted pricing considerations as they work to improve their models.

Few lawyers would be foolish enough to let an AI make their arguments, but one already did, and Judge Brantley Starr is taking steps to ensure that debacle isn’t repeated in his courtroom.

REGULATIONS

As artificial intelligence (AI) continues to revolutionize health care, the need to protect personal health data has become increasingly crucial. But intervening attempts to protect personal health data may also slow health care AI development.

Policy-makers should take lessons from history to curtail risk.

The European Union has used a transatlantic trade and technology talking shop to commit to moving fast and producing a draft Code of Conduct for artificial intelligence, working with US counterparts and in the hope that governments in other regions — including Indonesia and India — will want to get involved.

MACHINE LEARNING

AAMI and the British Standards Institute (BSI) have jointly published new guidance documents on performing risk management for machine learning or artificial intelligence incorporating medical devices.

One of the most prevalent cancers in women is breast cancer. Early and accurate detection can decrease the mortality rate associated with breast cancer. Governments and health organizations emphasize the significance of early breast cancer screening since it is associated to a greater variety of available treatments and a higher chance of survival. …. Breast “infrared thermal” imaging is one of the imaging modalities., a screening instrument used to measure the temperature distribution of breast tissue, and even though it has not been used as extensively as mammography it demonstrated encouraging outcomes when utilized for early detection. It also has several advantages, as it is safe, painless, non-invasive, and inexpensive. The literature showed that the use of thermal images with deep neural networks improved the accuracy of early diagnosis of breast malformations.

Most organizations are not able to be fully data driven because the majority of the data generated these days is highly unstructured and when it comes to large-scale analytics across data types and formats, there are some limiting factors for enterprise applications: 1) data storage and management, 2) infrastructure management and 3) availability of data science resources. With BigQuery’s new unstructured data analysis feature, you can now store, process, analyze, model, predict, with unstructured data, and combine it with structured data in queries. Best of all, you can do all of this in no-code SQL-only steps.

Unstructured data such as images, speech and textual data can be notoriously difficult to manage, and even harder to analyze. The analysis of unstructured data includes use cases such as extracting text from images using OCR, sentiment analysis on customer reviews and simplifying translation for analytics. All of this data needs to be stored, managed and made available for machine learning.

The new BigQuery ML inference engine empowers practitioners to run inferences on unstructured data using pre-trained AI models. The results of these inferences can be analyzed to extract insights and improve decision making. This can all be done in BigQuery, using just a few lines of SQL.

NATURAL LANGUAGE PROCESSING

The telecom sector is transforming how communication happens. Striving to provide reliable, uninterrupted service, businesses are tackling the challenge of delivering an optimal customer experience.

RESEARCH

Researchers at Gladstone Institutes, the Broad Institute of MIT and Harvard, and Dana-Farber Cancer Institute have turned to artificial intelligence (AI) to help them understand how large networks of interconnected human genes control the function of cells, and how disruptions in those networks cause disease.

Recent structural collapses, including tragedies in Surfside, Florida, Pittsburgh, New York City and Davenport. Iowa, have centered the need for more frequent and thorough inspections of aging buildings and infrastructure across the country. But inspections are time-consuming, and often inconsistent, processes, heavily dependent on the judgment of inspectors. Researchers at Drexel University and the State University of New York at Buffalo are trying to make the process more efficient and definitive by using artificial intelligence, combined with a classic mathematical method for quantifying web-like networks, to determine how damaged a concrete structure is, based solely on its pattern of cracking.

Artificial intelligence (AI) technologies such as statistical machine learning have been used in forensic psychiatry mainly for the prediction of aggressive behavior and recidivism but little has been done in the area of capacity assessment. However, the statistical machine learning model responses are difficult to interpret and explain, which presents problems with regard to the new General Data Protection Regulation (GDPR) of the European Union. In this Perspective we present a framework for an AI decision support tool for TC assessment.

USE CASES

The public sector is investing heavily on artificial intelligence and machine learning initiatives. Deloitte AI Institute reported that 60% of government AI and data analytics investments aim to directly impact real-time operational decisions and outcomes by 2024. From automating redundant tasks to increasing the quality of services offered to citizens, public sector institutions have a wide range of applications where they could implement AI.

INDUSTRY CLOUD PLATFORM

Know your prompt tuning from your prompt design? Here are key generative AI terms and how they affect your organization.

AI is transforming industries, automating processes, and opening new opportunities for innovation in the rapidly evolving technological landscape. As more businesses recognize the value of incorporating AI into their operations, they face the challenge of implementing these technologies efficiently, effectively, and reliably.

Enter NVIDIA AI Enterprise, a comprehensive software suite designed to help organizations implement enterprise-ready AI, machine learning (ML), and data analytics at scale with security, reliability, API stability, and enterprise-grade support.

RISK AND SECURITY MANAGEMENT

We are launching the Cybersecurity Grant Program—a $1M initiative to boost and quantify AI-powered cybersecurity capabilities and to foster high-level AI and cybersecurity discourse.

For most organizations, it’s time for the relationship between their boardroom and their cybersecurity practice to evolve — especially in the context of digital transformation to the cloud. This has been a regular dialogue of late, driven in part by corporate risk processes, potential regulations, and ongoing drum beats to improve cybersecurity risk mitigation, all while managing the enterprise's strategic, competitive, and defensive risks.

CUSTOMER SERVICE

Despite ChatGPT's customer service benefits, organizations must understand the technology's risks, such as fabricated information, bias and security concerns.

RESOURCES

Led by Andrew Ng, this course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs, practical advice); reinforcement learning and adaptive control. The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing. Last Updated - Mar 6, 2023