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🪄 Weekly Magic AI: Apple’s BIG healthcare plans | Regulation of AI companies | AI in software development
Top AI news of the week, Magic AI tool of the week, and article of the week
Hi AI Enthusiasts,
Welcome to our weekly email newsletter, where we bring you the latest updates on Artificial Intelligence (AI) in an accessible way. This week's Magic AI tool can coach you to become a top speaker. Be curious!
Top AI news of the week
Apple wants to use AI for next-generation health monitoring
Apple plans to introduce new health-related features for the Apple Watch in 2024, along with a paid health service. These features include a sensor to detect elevated blood pressure, although the initial version won’t display exact measurements. However, the company is working on a system to make more accurate measurements possible. It will also be linked to a new blood pressure diary so that a user can record what happened when high blood pressure occurred.
Apple is also working on a system to detect sleep apnea by monitoring sleeping and breathing patterns and recommending follow-up with a doctor if necessary. Additionally, Apple is developing new features to turn AirPods into over-the-counter hearing aids. In addition, they are developing a digital health coaching service that uses AI and user data to create personalized workout and eating plans. The company also wants to offer workout features for its upcoming Vision Pro headset.
Apple has long expressed an interest in expanding into the healthcare sector. However, plans have been hindered by concerns from top executives about the potential negative impact on the company's reputation.
Slip-ups in the high-stakes field of healthcare could tarnish the perception of the company. However, it is an essential area, and the effective use of AI in healthcare is positive for us all.
Above all, we find the functionality of blood pressure measurements very useful. What is your opinion on this?
🤖 USA: Regulation of AI companies
President Biden has introduced the first regulations on AI systems in the US, which include testing requirements and measures to prevent discrimination. Developers of high-risk AI must share safety testing results with the government before releasing their products. Federal agencies are instructed to establish standards for responsible AI development, including labeling AI-generated content. The regulations also aim to address potential bias in algorithms used in areas like housing and benefits programs.
The Order further focuses on protecting Americans’ privacy, advancing equity and civil rights, supporting consumers, patients, and students, promoting innovation and competition, and ensuring responsible government use of AI.
The scope of the executive order is initially limited, as it is aimed at various federal authorities, which in turn are to hold companies to account first. The US government agreed on the content with the 15 leading technology companies in the summer.
It is a difficult balance between enforcement and suppression of innovation. Andrew Ng has also spoken out in this context. In an interview with the Australian Financial Review, he accused big tech of playing up extinction scenarios for AI to push ahead with regulation. He believes that the big AI companies want stricter rules in order to destroy open-source competitors and avoid competition.
What’s your opinion about AI regulations?
Google Brain founder says big tech is lying about AI extinction danger - Australian Financial Review
👨🏽💻 AI in software development - Overestimated?
The end is near! AI will soon take over software development. These statements are overblown and show that many people don't know what software development is about. It's clear: AI helps to write source code. However, software development is not just about writing code! It's so much more.
It is essential to write valuable code that meets the requirements. For this, you need to know the technical requirements. In practice, however, the requirements are usually unclear. That makes it difficult to develop software from them in a successful way. Software developers need to understand the domain very well in order to build valuable software. Requirements can also change, and you need to adapt the code in certain places.
The maintainability and expandability of code bases are crucial in large software applications. The useful and secure implementation of software applications requires human intervention despite AI support. That also shows a study in which participants had access to an AI assistant from OpenAI. Participants with AI assistants wrote significantly less secure code than participants without access.
AI primarily serves as support for writing and understanding code quickly. Understanding a particular domain is still a human task. AI can then significantly accelerate the development process during implementation.
👉🏽 The story is not that AI replaces programmers. It’s that programmers using AI will replace programmers without AI. AI is just a tool. Nothing more! Take your chance and use AI to your advantage.
KI in der Softwareentwicklung: Überschätzt (German) - Heise Online
Magic AI tool of the week
🪄 Yoodli - Become a top speaker with AI
Yoodli is a private, real-time, judgment-free language coach. With Yoodli, you get real-time, non-distracting alerts to slow down, stop rambling, and avoid filler words during online meetings. And the best part? No one else will know you’re using Yoodli, it just analyses your voice. Professionals at Google, McKinsey, and others already use Yoodli. At the end of the meeting, you’ll get a report with suggestions for improvement, and you can track your progress over time! You can test it for free.
Go to app.yoodli.ai and download the free desktop app
Connect your calendar
Yoodli automatically gives you coaching during the calls.
Article of the week
Project controlling is essential in data science projects, as the time required and costs are often difficult to estimate. Clients want to implement their use cases efficiently and successfully with machine learning methods. You can positively influence consistent project control with meaningful key performance indicators (KPIs). According to some studies, 85% of all data science projects fail, which requires early identification of obstacles.
In classical IT projects, many KPIs exist for project controlling, but these are not sufficient for data science projects. For this reason, in this article we present basic KPIs for IT projects and analyse them with regard to data science projects.
Thanks for reading, and see you next time.
- Tinz Twins
P.S. Have a nice weekend!
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