Apple’s AI Revolution: Why Some Creators Are Pushing Back

Apple’s AI Revolution: Why Some Creators Are Pushing Back

Why are app creators opposing Apple intelligence?

1.Difficult to add new users

App developers say that this change is disastrous for social apps. They believe that this change will make it difficult for new social apps to add new users quickly, because apps will now get only limited contacts from each user. Big apps like Facebook and Instagram, which already have a huge network of users, can take advantage of this change. At the same time, it will become difficult for small and new apps to make their place quickly and connect with new users, because they will have less contact data.

2.Benefit of user friendly content

In the development of social apps, it is important to add early users quickly. The new changes in iOS 18 can slow down this process, small apps will face difficulty in adding new users. It is believed that in the coming time, this change can also affect the new trend of social apps. Apps which are based on the network of users can get into trouble due to this privacy policy of AI. On the contrary, apps which show content to users according to their choice can benefit from this change.

What is AlfaFold AI ?

Imagine attempting to solve a very complex puzzle that could take a lifetime, yet might remain unsolved. Now imagine having a tool that could solve such puzzles in a matter of moments. This tool is AlphaFold, an artificial intelligence (AI) system developed by DeepMind researchers Demis Hassabis and John Jumper, who have been announced as winners of the 2024 Nobel Prize in Chemistry. Both researchers work at Google DeepMind in London.

What does AlphaFold do?

Proteins have intricate structures that determine their functions, and AlphaFold predicts these structures with high accuracy. This ability helps in understanding how a protein folds and its 3D shape. AlphaFold was developed in the Google DeepMind lab, and its latest version (Version 3) is now available in the market.

Breakthrough in COVID-19:

AlphaFold was used to understand the structure of the COVID-19 virus's proteins, aiding in the development of vaccines and drugs. Companies like Pfizer-BioNTech and Moderna utilized AlphaFold’s data for their COVID-19 vaccine development. . This research may aid in developing new treatments.

AlphaFold also shows potential in creating new antibiotics with fewer side effects and in research for diseases like Alzheimer's, Parkinson’s, and heart conditions.

How Does AlphaFold Work?

Like other AI models, AlphaFold analyzes a large amount of data to predict the structure of proteins. This process is highly complex but enables AlphaFold to perform with speed and precision. The model requires extensive molecular information, including details about primary, secondary, tertiary, and quaternary structures. Scientists collect and organize this data to enhance accuracy.

AlphaFold-3 incorporates a new feature called diffusion networks, a technique also used in AI image generators. Its operation begins with incomplete structural information about a molecule and progresses to a precise and accurate 3D representation of its structure.

Benefits of AlphaFold:

1. Development of New Drugs: Scientists can create innovative medicines using AlphaFold.
2. Easier Treatment Discovery: It simplifies the search for cures for various diseases.
3. Improved Food Quality: Helps produce high-quality food products.
4. Environmental Conservation: AlphaFold-3 can assist in saving the environment.

Applications of AlphaFold:

• Drug Design: Accurately studies interactions between proteins and ligands, aiding new drug discoveries.
• Biological Research: Enhances understanding of biological processes like gene expression, cell division, and immune responses.
• Antibody-Protein Binding: Identifies interactions between antibodies and proteins, essential for medical advancements.
• Study of Biological Structures: Examines the network of molecules to understand their complete structure and functionality.

Diffrence between ChatGpt and Turbo:

Aspect	Comparison
	Turbo	ChatGPT
Process Speed	Very fast, ideal for generating content quickly.	Slower, suitable for detailed responses but may take time.
Price	Cost-effective for generating large volumes of content.	Slightly more expensive for extensive blogging tasks.
Uses	Excellent for bulk content creation, keyword research, and summaries.	Great for creating engaging posts, answering queries, and brainstorming ideas.
	Both tools cater to different blogging needs based on the user’s style.
Latency	Low latency ensures faster draft generation for posts.	Higher latency, acceptable for general blogging needs.
Input Handling	Supports both text and image processing, useful for visual content creation.	Primarily text-based; limited image support in newer versions.
Hardware Efficiency	Optimized for productivity, handles large-scale tasks efficiently.	Works on older systems, suitable for casual blogging tasks.
Cost Efficiency	Ideal for bloggers managing multiple projects or creating frequent posts.	Affordable for occasional blogging, costs may add up for heavy use.
	Turbo is more budget-friendly for extensive projects, while ChatGPT is great for smaller-scale use.
User Group	Best for professional bloggers or those managing content for multiple clients.	Perfect for hobbyist bloggers and individual users.
Accuracy of Content	Provides precise and structured content quickly.	Offers detailed and creative content, but at a slower pace.
Best For	SEO-focused content, bulk post creation, and quick edits.	Writing personalized, creative, and conversational blog posts.