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It's safe to say that AI is top of mind for enterprises -- assuming it wasn't before. According to a recent Accenture survey, 63% of organizations are now prioritizing AI over all other digital technologies. Accenture's stats are similar to McKinsey's, which show that more than half of companies are investing more than 5% of their digital budgets in AI. Sixty-three percent, meanwhile, say that they expect their investment to increase over the next three years. The enthusiasm is reflected in the growing financing AI startups have been able to attract.

Is ChatGPT the beginning of the Star Trek vision: We'll just tell the computer what we want it to do? The short answer is: Not right now, and probably not any time soon. That's because the types of coding problems at which ChatGPT seems to excel are common ones. If you ask it to do something that's been done a ton of times before, then sure, it'll do a very good job. These have been coded a bajillion times before, and they're all online. OpenAI trained its models on all that existing code.

Machine vision analysis of echocardiography images (echo) has vital recent advances. Echocardiography images are ultrasound scans that present the cardiac structure and function that becomes helpful in a significant measure of eight standard echo views, namely A2C, A3C, A4C, A5C, PLAX, PSAA, PSAP, PASM of the Cardiac cycle, and also identifies the disorders. In this research, we introduce a vision model for echo analysis with a deep convolutional neural network protected by the U-Net, trained to phase the echoes, and extract information of the right ventricle, left atrium, aorta, septum, and outer internal organ wall. The data includes image bundles; input to the CNN model predicts the cardiac structure by a softmax function into different categories, which becomes an input to a U-Net architecture that encodes and decodes the layers and foretells the functioning of the heart through segmentation. In summary, the research covers designed architecture that presents state-of-the-art for investigating echocardiography information with its benefits and drawbacks continued by future work.

The recent incident of ChatGPT, an advanced AI language model by OpenAI, inadvertently leaking user chat titles has raised concerns about user privacy and data protection in AI-driven platforms. Let's delve into the incident, its implications, and the essential steps required to ensure user privacy and trust in the age of AI. The leak involved the unintended exposure of user chat titles within the ChatGPT interface. Although the content of user conversations remained secure, the visible chat titles created the potential for unauthorized access to sensitive information. This incident has sparked a debate about the need for robust security measures and user privacy in AI-driven platforms.

Additionally, this approach also falls a bit short from an Explainable AI (XAI) perspective. While these models all have attention mechanisms that can be visualized, many academics have argued that this may not be explainable and this is an active field of debate. Denis Vorotyntsev has made a great article summarizing the debate and I highly encourage checking his article out as well [10]. In contrast to the attention-based approach of transformers, the other primary direction of tackling the forecasting problem is the neural basis expansion analysis approach first proposed by Oreshkin et.

One of the key benefits of machine-generated writing is its ability to quickly generate large amounts of content. This can be especially useful for businesses and organizations that regularly produce a large volume of content to maintain a solid online presence and attract potential customers. Using AI writing, these businesses and organizations can save time and resources that would otherwise be spent manually creating content. Another major advantage of AI writing is its ability to produce content that is optimized for search engines. Because AI writing algorithms are designed to understand and mimic human language, they can produce content that is easy for search engines to understand and rank highly in search results. This can help businesses and organizations improve their search engine rankings and drive more website traffic.

We have a very special guest on today's episode. We talk with Kirk Borne, a top AI influencer since 2013. From his LinkedIn Bio, Kirk is the Founder of the Data Leadership Group (Data Scientist. Consultant) and Advisor to DataPrime Inc., but in the episode, you will see his background exceeds much more than this! Generative AI is making a splash across the news with ChatGPT and other large language models.

A new study led by faculty at the University of Georgia demonstrates the potential of using artificial intelligence to transform tuberculosis treatment in low-resource communities. And while the study focused on TB patients, it has applications across the health care sector, freeing up health care workers to perform other necessary tasks. Growing evidence has demonstrated the potential for AI to increase productivity, reduce health care worker burnout, and improve quality of care in clinical settings. The study, which was published last month in the Journal of Medical Internet Research AI, pilots the use of AI to watch thousands of submitted videos of TB patients taking their medication. This application could automate the job of a health care worker watching a patient take their pill at a clinic, known as directly observed therapy (DOT).