Рубрика: AI

In a remarkable display of precision and determination, the Indian Space Research Organisation (ISRO) achieved a significant milestone in its ambitious Gaganyaan Mission – the endeavour to send Indian astronauts into space. The latest test, part of this mission, involved the launch of the Test Vehicle (TV-D1), powered by a liquid-propelled single-stage rocket, from the Satish Dhawan Space Centre.

The focus of this mission was the validation of the Crew Escape System, a critical component for the safety of Indian astronauts during spaceflight. This system comprises various motors, including low-altitude, high-altitude, and jettisoning motors, designed to safely eject astronauts from the vehicle in case of an emergency. ISRO Chief S. Somnath declared the mission complete with all objectives achieved.

“Im very happy to announce the completion of TV-D1 mission,” said the ISRO chief, addressing the team dedicated to the launch.

Quick Challenge Recovery

The journey to this moment was not without its challenges. Initially scheduled for launch at 8:00 a.m., the mission experienced a delay due to adverse weather conditions. However, what transpired next is a testament to the dedication and problem-solving capabilities of the ISRO team.

Within an hour of experiencing a glitch during the first launch attempt, ISRO identified and resolved the issue. The team’s swift response and ability to rectify the problem within such a short timeframe were truly remarkable.

The flight sequence for TV-D1 commenced with the launch, and six seconds into the flight, the fin-enabling system was activated. The Crew Escape System Pillbox was triggered at a speed of Mach 1.25, approximately 11.8 km above the Earth’s surface. The High Energy Motor (HEM) fired to propel the vehicle further into the atmosphere.

At about 61.1 seconds after launch, when the vehicle reached a Mach number of 1.21 at an altitude of 11.9 km, the Crew Escape System separated from the rocket booster. The Crew Module then separated from the Crew Escape System at an altitude of 16.9 km, travelling at a speed of 550 km per hour. Subsequently, the drogue parachute deployed, slowing the vehicle’s descent.

“Mission Gaganyaan TV D1 Test Flight is accomplished. Crew Escape System performed as intended. Mission Gaganyaan gets off on a successful note,” ISRO announced.

The Crew Escape System, akin to fighter jet ejection seats, plays a vital role in safeguarding astronauts during spaceflight anomalies. It operates automatically, detecting malfunctions or issues immediately after liftoff, prior to rocket stage separation. This rapid ejection minimises potential risks during the early phases of ascent.

The successful test launch was crucial for validating the system that would be indispensable during the Gaganyaan Mission‘s initial phase. It will be responsible for jettisoning the Crew Module, with astronauts on board, to a safe distance in the event of a critical problem. The Crew Module will then separate and safely splash down in the sea, aided by parachutes.

Data gathered from this test will further enhance the system’s development, ensuring its reliability when astronauts embark on the Gaganyaan Mission.

This achievement underscores India’s progress toward realizing its dream of sending its first astronauts into space from its own territory. The Indian space agency plans to launch its first astronauts, who are currently undergoing training, into space by 2025 and aims to reach the Moon by 2040.

ISRO’s unwavering commitment, ability to overcome challenges, and rapid problem-solving abilities have taken India one step closer to a historic space mission, marking a significant milestone in the country’s space exploration journey.

The post ISRO Achieves Milestone in Gaganyaan Mission with Successful Escape System Test appeared first on Analytics India Magazine.

“It is not inferior in any aspect to GPT-4,” Baidu’s CEO Robin Li boldly claimed, stating that the latest model was “significantly improved” compared to its original Ernie Bot, at the recent launch of Ernie 4.0.

Robin Li demonstrated Ernie 4.0 at the company’s annual Baidu World conference in Beijing. He said the model has achieved comprehension, reasoning, memory and generation, which uses algorithms to produce and create new content. Li said that Ernie 4.0 was able to understand complex questions and instructions and apply reasoning and logic to generate answers to questions.

While many speculated that OpenAI’s challenger might emerge from the United States, it seems the competition has arisen from China, Baidu’s Ernie 4. Ernie, not to be confused with the friendly Sesame Street character, has been making waves in the AI community, and this latest iteration promises to be a game-changer.

ERNIE 4.0’s performance has improved by nearly 30% since its beta testing in September and is currently available in an invitation-only beta.

How Far Has Ernie Actually Come

What sets Ernie 4 apart from its predecessors is its planned integration into almost all of Baidu’s products. This includes prominent services like search engines, maps, and cloud computing. Such widespread integration is a significant move in the AI world, underlining Baidu’s confidence in Ernie 4’s abilities.

ERNIE 4.0 and the user-facing text chatbot, ERNIE Bot, were designed for understanding, generating, reasoning, and memorising. Live demonstrations showcased ERNIE Bot’s ability to understand complex human requests and generate various types of content, such as text, images, and videos. Clearly, Baidu realises that the multimodal race is on.

In a live demonstration, Li prompted Ernie 4.0 to generate advertising materials including advertising posters and a marketing video. He also asked Ernie 4.0 to come up with a martial arts novel complete with characters with various personalities.

The architecture of Ernie 4 is a topic of interest. The model relies on self-supervised pre-training and innovative techniques for data processing. It involves a unique approach to constructing datasets and employs techniques such as natural language processing. The model is said to harness massive datasets, including the impressive A3 Corpus, comprising around 10 billion parameters.

Notably, Ernie 4 emphasises green and sustainable AI. It adopts an online distillation technique, which seeks to transfer knowledge signals from a “teacher model” to multiple “student models” of varying sizes. This approach results in significant power savings compared to traditional distillation methods. Ernie 4 even introduces a “teacher assistant model” to bridge the knowledge gap between deep and shallow models.

Moreover, the model boasts an end-to-end adaptive distributed training framework, designed for versatile and adaptive usage in industrial applications and production environments. This framework considers resource allocation, model partition, task placement, and distributed execution, making it an attractive choice for various real-world scenarios.

Lukewarm at Best

Baidu introduced its first Ernie Bot in March. However, there is a notable challenge when it comes to Ernie 4. Unlike its competitors, it is not yet publicly available, which has left tech enthusiasts and developers eager for a glimpse of its capabilities. Access to source code or demos has been limited, leaving much to the imagination.

However, analysts expressed disappointment with the launch of Ernie 4.0, highlighting a lack of major improvements compared to its previous version. Baidu’s Hong Kong shares also fell by 1.32% during morning trading, underperforming the broader Hang Seng Index’s 0.7% rise.

According to Lu Yanxia, an analyst at IDC, while substantial enhancements may become evident with hands-on use, specific upgrades in Ernie 4.0 are not immediately apparent.

Potential Disruption

China has recently sought to regulate the generative AI industry, requiring companies to carry out security reviews and obtain approvals before publicly launching their products. Companies that provide such AI services must also comply with government requests for technology and data.

Regardless, Baidu had also recently raised $140 million, however, analysts’ and market reaction to the announcement indicates that the Chinese giant has a long way to go.

Baidu also announced the integration of AI technology into its suite of products, including Baidu Search, Baidu GBI, Infoflow, Baidu Wenku, Baidu Maps, Baidu Drive, and its business intelligence offerings for enterprise customers—to enhance user experiences and productivity.

However, one thing is clear, through its integration into a variety of Baidu’s offerings it will look to challenge various entities. For instance, many believe Ernie 4.0 is set to disrupt marketing, and will challenge traditional creative platforms like Adobe and Canva—-who will have to step up their game.

The post Baidu’s Ernie 4 Aims to Outshine GPT-4 appeared first on Analytics India Magazine.

In the last few months, there has been a significant rise in the research work related to autonomous AI agents, particularly in the context of large language models (LLMs), changing the way one interacts with the internet or web – be it sending emails, negotiating, making products, purchases, fulfilling orders, or even booking flight tickets, or even how LLMs are going to be built in the near future.

LLMs currently depend on human guidance and lack autonomous reasoning, whereas autonomous agents can operate independently, making real-time decisions and adapting to changing scenarios. One exciting application of autonomous agents is their ability to enhance LLMs’ performance. They collaborate in multi-agent conversations, enabling LLMs to improve through feedback and reasoning exchange.

Microsoft recently came up with AutoGen, a framework that enables building LLM applications using multiple agents that would be able to talk to each other. Similarly, Google DeepMind recently published a paper ‘How FaR Are Large Language Models From Agents with Theory-of-Mind?​’ Even Meta’s Shepherd: A Critic for Language Model Generation​ talks about the same autonomous AI agents augmenting and doing tasks all by themselves.

Other papers also include SELF: Language-Driven Self-Evolution for Large Language Model and SelfEvolve: A Code Evolution Framework via Large Language Models.

OpenAI is also gearing up to launch something similar next month at DevDay, touted to be called JARVIS.

Here is a list of most recent autonomous AI agents:.

AutoGen

Microsoft’s AutoGen, for instance, leverages LLMs to create versatile agents capable of learning, adapting, and even coding. This fusion of abilities, coupled with features like caching and human intervention, empowers AI systems to evolve and thrive.

AutoGen simplifies the creation of next-gen LLM applications, automating and optimising complex workflows. This AI agent supports diverse conversation patterns, and developers can customise agent interactions. It offers a variety of working systems for different applications and can replace OpenAI’s tools for enhanced inference APIs.

MusicAgent

Microsoft researchers recently introduced MusicAgent, an LLM-powered autonomous agent in the music domain. This AI agent is said to help developers to automatically analyse user requests and select appropriate tools as solutions. Their new framework directly integrates numerous music-related tools from various sources, including Hugging Face, GitHub, Web search, etc.

In addition to this, the researchers have also adapted the autonomous workflow to enable better compatibility in musical tasks, allowing users to extend its toolset. Looks to integrate more music-related functions into MusicAgent.

Mini AGI

MiniAGI is a straightforward autonomous agent that works seamlessly with GPT-3.5-Turbo and GPT-4. It utilises a sturdy prompt along with a minimal toolkit, a chain of thoughts, and a short-term memory incorporating summarization. Additionally, it has the ability for inner monologue and self-critique.

MultiGPT

Multi-GPT is an experimental multi-agent system featuring “expertGPTs” that collaborate to accomplish tasks. Each expertGPT possesses individual short and long-term memory and the ability to communicate with others. Users can assign tasks, and the expertGPTs will work together to complete them.

The system offers internet access for information gathering and searching. It manages short and long-term memory efficiently. It uses GPT-4 instances for text generation, provides access to popular websites and platforms, and includes file storage and summarization using GPT-3.5. This makes Multi-GPT a versatile tool for various tasks and data management needs.

BeeBot

BeeBot is an autonomous AI assistant designed to streamline and automate a wide range of practical tasks. With BeeBot, users can experience the convenience of selecting tools via AutoPack, offering the flexibility to acquire additional tools as tasks evolve. Moreover, the inclusion of built-in persistence ensures that BeeBot can remember and recall information, making it an even more reliable assistant.

It can easily work with different systems and services thanks to its REST API, which follows a common standard called e2b. BeeBot also keeps you in the loop by using a websocket server to share updates in real-time. It’s adaptable for different ways of storing files, like in memory, on your computer, or in a database.

BabyAGI

Baby AGI, a Python script, streamlines task management by using OpenAI and Pinecone APIs alongside the LangChain framework. This AI-driven system excels in creating, organising, prioritising, and executing tasks based on predefined objectives, all learned from past tasks.

Baby AGI leverages OpenAI’s natural language processing (NLP) capabilities to craft new tasks that align with set objectives. Pinecone serves as the repository for storing task results and retrieving context, while the LangChain framework handles decision-making.

The post 6 Must-Know Autonomous AI Agents appeared first on Analytics India Magazine.

With the release of update 23H2, computers running Windows 11 will now have Microsoft’s generative AI platform Copilot installed and ready for user interaction. For many, this is something to celebrate, but for others, Windows Copilot is something to be dreaded and disabled as soon as possible.

Although disabling Windows Copilot is possible, it isn’t as easy as flipping a toggle switch on a settings menu. The procedure requires the editing of the Windows Registry File or a modification to default settings through the Group Policy Editor. We walk you step-by-step through both processes.

Jump to:

• Why would someone want to disable Windows Copilot?
• How to disable Windows Copilot through the Windows Registry File Editor
• How to disable Windows Copilot through the Group Policy Editor

Why would someone want to disable Windows Copilot?

There are several reasons why some individuals and businesses may choose to disable generative AIs in general and Windows Copilot specifically.

1. Philosophical reasons

Many people and organizations are opposed to generative AI platforms such as Windows Copilot because they find artificial intelligence dangerous, unpredictable and morally questionable. These people and organizations often want to avoid AI completely on these philosophical grounds.

2. Data protection

Some businesses and organizations operate under strict data protection rules and regulations that must be complied with at all times and in all cases. For those businesses, AI in its current iteration is too unpredictable and too prone to unforeseen biases. In addition, not enough is known about how such platforms use and share sensitive internal data. The security of AI-generated results cannot be trusted and verified properly to meet compliance requirements. Therefore, AI must be eliminated from all production and primary systems in these organizations, at least for now.

3. Practical reasons such as computing resources and power

For other individuals and businesses, disabling Windows Copilot is merely a practical matter. Copilot, like all generative AI platforms, requires a considerable amount of computing resources and power. According to the Windows 11 Task Manager (Figure A), Copilot uses over 260MB of RAM while running in the background, which doesn’t include the potential use of CPU and networking resources during an inquiry. Depending on the computer equipment involved, these resource requirements could be enough to slow down performance to an unacceptable level. In such cases, Windows Copilot may be a background process that needs to be disabled.

Figure A

How to disable Windows Copilot through the Windows Registry File Editor

Disclaimer: Editing the Windows Registry file is a serious undertaking. A corrupted Windows Registry file could render your computer inoperable, requiring a reinstallation of the Windows operating system and potential loss of data. Back up the Windows Registry file and create a valid restore point before you proceed.

To make edits in the Windows 11 Registry File, type “regedit” into the Windows 11 search tool. From the results, choose the Regedit app and then use the left-hand window to navigate to this key HKEY_CURRENT_USERSoftwarePoliciesMicrosoftWindows (Figure B).

Figure B

We must create a new key. In the left-hand navigation window, right click the Windows key and select New | Key from the context menu and give it the name WindowsCopilot (Figure C).

Figure C

Now, right click the new WindowsCopilot key you just created and select New | DWORD (32-bit) Value from the context menu and give this new subkey the name TurnOffWindowsCopilot (Figure D).

Figure D

Now, double-click the TurnOffWindowsCopilot subkey you just created and change the Value Data entry to 1 and click OK (Figure E).

Figure E

Exit out of Regedit and restart your computer to disable Windows Copilot.

If you want to reverse the process and enable Windows Copilot again, change the Data value of the TurnOffWindowsCopilot subkey to 0.

How to disable Windows Copilot through the Group Policy Editor

If you are using Windows 11 Pro or an Enterprise version of the operating system, you can use the Group Policy Editor to disable Windows Copilot. Type “group policy editor” into the Windows search tool and select the correct app from the results.

Using the left-hand window, navigate to this policy: User Configuration | Administrative Templates | Windows Components | Windows Copilot (Figure F).

Figure F

In the right-hand window, double-click the Turn Off Windows Copilot policy, select the Enabled radio button, click Apply (Figure G) and then click OK.

Figure G

Exit the Group Policy Editor and restart your computer. With this policy enabled, Windows Copilot will be disabled.

To re-enable Windows Copilot with the Group Policy Editor, change this setting to Disabled and restart your computer.

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As someone who has a full-time job by day and works on multiple side hustles by night (ranging from creating written content, online courses, and more recently, a tech YouTube channel), I am always looking for new ways to generate passive income.

So when ChatGPT was released, I was the first to scour the Internet for advice on using the tool to make more money.

Unsurprisingly, much of the recommendations I found online ranged from vague and unactionable to downright unethical.

Apparently, thousands of people have started flooding Amazon with AI-generated books, causing the platform to impose self-publishing restrictions and remove suspected AI-generated content in droves.

After weeding through the sea of bad advice on the Internet, however, I did find some pretty useful ways in which people have monetized ChatGPT and generative AI tools.

I’ve even started incorporating some of this advice into my own workflows (and managed to almost double my online income in the past month).

In this article, I will share my learnings and provide you with 3 realistic ways in which you can start making money with ChatGPT.

1. Create Faceless Social Media Content

The creator economy is exploding.

There is tremendous opportunity to make money by building a personal brand and generating content that others find valuable.

However, a lot of time (and money) goes into content creation.

A single YouTube video requires coming up with a topic idea, writing a script, spending hours speaking in front of a camera, and editing in post-production.

It also takes a lot of trial and error to figure out the ideal video background, lighting, and audio. And for many of us, it can seem daunting and unnatural to sit down and speak to a camera.

Generative AI is a game-changer in the content creation space. You can use ChatGPT to perform topic research, generate a compelling “hook” for your YouTube video, and even optimize your content so that it ranks well on Google.

Then, you can use a tool like Synthesia to create an AI avatar and voiceover for your videos, removing the need for you to appear in them yourself:

Image by Author

Finally, to promote your content across various platforms, simply use a tool like Opus AI to take parts of your YouTube video and transform it into short-form content which can be reused across sites like TikTok and Instagram.

Of course, you want to set yourself apart from the AI-generated content flooding the Internet, which means putting in a decent amount of research into your videos and selecting a niche.

ChatGPT and generative AI should speed up the content creation process, and isn’t meant to replace your creativity and unique voice.

2. Online Courses

Online courses on generative AI tools like ChatGPT, Midjourney, and DALLE are exploding in popularity.

Note: Top Udemy instructors make over $1 million on the platform, while the average creator earns around $3,306 annually.

Here are some popular, beginner-friendly generative AI courses on Udemy:

Image by Author

Look at the number of students enrolled into the above online courses!

This should tell you that individuals and organizations are willing to invest heavily in learning more about AI.

Of course, this doesn’t mean that just anyone can go about creating an introductory ChatGPT course with regurgitated content.

What you should do is this:

Find a niche

If you work in marketing, for example, you already have subject matter expertise that sets you apart from an everyday person.

Now, start learning to incorporate ChatGPT into your daily workflows. Is there an aspect of your job that generative AI can automate?

For instance, AI has become really good at generating SEO-friendly content that even Google cannot reliably tell apart from human written articles.

Using this knowledge, can you teach small business owners to market their products effectively without having to hire a content creator or SEO expert?

This is a skill that many people will pay to learn, because it teaches them to save money and enhance business outcomes in the long term.

With less than 2 hours of video content, for instance, this Udemy course already has 5,000 students by simply teaching people how to learn programming with ChatGPT:

Image by Author

If you already have a marketable skill, you can make a lot of money by teaching other people how to hone that skill with ChatGPT.

And in today’s creator economy, it’s never been easier to start!

All you need to do is enroll into a platform like Udemy or Teachable and start building the course.

3. Prompt Engineering and AI Consultation

If you’ve been following announcements in the field of AI lately, you might have heard of the hot new job in the market called a “prompt engineer.”

According to Business Insider, these roles pay up to $375,000/year and don’t even require a tech degree.

So… does this mean that anyone can make this kind of money by simply typing prompts into ChatGPT? What’s the catch?

Although it’s a relatively new field that will continue to evolve in the next few years, the main role of a prompt engineer is to improve business outcomes and maximize efficiency with generative AI.

Most prompt engineering roles require subject matter expertise. You must be able to apply prompting techniques to solve industry-specific problems.

For instance, if you work in marketing, you might be hired to get ChatGPT to create SEO-friendly content that is relevant to the business and accurate.

AI research company, Anthropic, is actively hiring prompt engineers and don’t have a fixed set of requirements that you need to meet to apply for the job.

The job listing states that they will consider applicants who are able to make strong enough cases for themselves, saying,

“if you haven’t done much in the way of prompt engineering yet, you can best demonstrate your prompt engineering skills by spending some time experimenting with Claude or GPT3 and showing that you’ve managed to get complex behaviors from a series of well crafted prompts.”

The company’s representatives go on to mention that logic and reasoning capabilities are the most important traits of a prompt engineer, and that having programming knowledge or a machine learning background is an added advantage when applying for this position.

And although many of these requirements seem extremely vague, it does seem like prompt engineering is a lucrative skill that people in various domains will benefit from.

Even if you don’t actually end up getting a job as a prompt engineer, this is a skill that businesses will pay to learn, since employees are still struggling with AI adoption.

You can offer consulting services around generative AI and prompt engineering in a specific niche like marketing or finance.

As someone who has offered data and machine learning consulting services to organizations in the past, I successfully got clients by building an online presence and creating a website, which I talk about in a separate article.

Also, if building a brand from scratch seems daunting, here is a list of AI tools that can help you automate the process.

This Guy Used ChatGPT to Turn $100 Into a Business in One Day

I’d like to end this article by pointing you to the story of Jackson Greathouse Fall, the guy who used generative AI to launch his business in one day by asking ChatGPT to turn $100 into “as much money as possible.”

He followed the chatbot’s instructions and managed to raise $1378 for his company in just one day. As of mid-March, the company was valued at $25,000.

You can read the entire story on Business Insider.

Natassha Selvaraj is a self-taught data scientist with a passion for writing. You can connect with her on LinkedIn.

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