How Apple Vision Pro Will Change DS?
💭Some Thoughts on Apple’s New Tech in DS
The internet is full with the Vision Pro these days. How it can affect our beloved industry?
Interactive Visuals
The biggest advantage of Apple VP is the AR experience. You can interact with things in the 3D world. This will enable interactive data visualizations, but not in the context we experienced before. You will be able to walk between the bars in a bar chart.
Collaborative Work
The above can help collaborative data exploration and decision-making processes. Your team will be able to sit around the same chart in the shared virtual space.
Enhanced Data Collection
For fields that rely on visual data such as medical research, and environmental science, the Apple Vision Pro could offer new methods for gathering high-quality datasets.
Productivity
In VP you can use as many windows/screens as you want. Some will find this distracting, but for many, it will be a productivity boost. You can also block the world around you, so no distractions at work. You can ‘literally’ work from the Moon.
Interactive Learning
The best way to learn something is to interact with it. With AR and VR interactivity is on the highest level so far. High-tech teaching will never be the same after VP.
Spatial Data
Devices like the VP need to understand the environment and world around them to work safely and effectively. For this reason, there is a big need for Spatial Data and Computer Vision insights.
Simulation and Testing Environments
Developers could use the device to create simulated environments for testing AI algorithms and models.
😂Tweets to Laugh and Learn
If we talk about Apple VP and DS… 😂
At this moment AI is just so random… It is the dumbest thing but changes the world at the same time.
After some laughs it’s time to learn.
These are all awesome sites but don’t forget to practice what you learn with some projects! Go to Kaggle, download a dataset, and do some cool analysis to begin your portfolio of projects.
🎙️Podcast Notes
SDS 752: AI is Disadvantaging Job Applicants, But You Can Fight Back — with Hilke Schellmann
Key takeaways:
AI in hiring processes
AI is increasingly used in the hiring process, but it is not always fair, accurate, or transparent.
Many AI hiring systems use biased data, flawed logic, and opaque methods to screen out applicants, especially women and minorities.
AI hiring systems use biased data, flawed logic, and opaque methods
Job seekers can fight back against unfair AI hiring practices by researching the company's hiring process, preparing for video interviews, and challenging the results if they feel discriminated against.
AI ethics
AI ethics is a crucial topic for data scientists, as they have the responsibility to design, develop, and deploy AI systems that are aligned with human values and social good.
Data Scientists have the responsibility to design, develop, and deploy AI systems that are aligned with human values and social good.
Data scientists should be aware of the potential harms and risks of AI, such as privacy violations, discrimination, manipulation, and deception, and take steps to mitigate them.
They should also be aware of the potential benefits and opportunities of AI, such as innovation, efficiency, and empowerment, and seek to maximize them.
Data scientists should follow best practices and standards for AI, such as fairness, accountability, and transparency, and adhere to ethical principles and codes of conduct.
Data scientists should communicate and collaborate with other stakeholders, such as employers, regulators, users, and society, to ensure that AI is used in a trustworthy and beneficial way.
👉 So remember, as a Data Scientist you have a big responsibility: you need to be aware of both harms and risks, and benefits and opportunities.