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Companies may unintentionally hurt their sales by including the words 'artificial intelligence' when describing their offerings that use the technology, according to a recent study. Researchers conducted experimental surveys with more than 1,000 adults in the U.S. to evaluate the relationship between AI disclosure and consumer behavior. The findings consistently showed products described as using artificial intelligence were less popular.

Connecting multiple AIs together and putting them in the body of a robot, similar to Star Wars' C-3PO or R2-D2, is the most likely way AI will become as intelligent as humans, according to a scientist from the University of Sheffield.

Using only on-board sensors and cameras, researcher Julián Estévez, from the Computational Intelligence Group (GIC) of the University of the Basque Country (UPV/EHU) has developed low-cost, autonomous, navigation technology to prevent two or more drones whose paths cross in mid-air from colliding with each other. He has achieved positive, encouraging results.

The Deus Robotics’ all-in-one warehouse automation solution offers AI-powered robots and an AI brain to manage any type of warehouse robots from a single platform."

Top Benefits & Use Cases of AI in Insurance Industry

Impact of Artificial Intelligence on The Insurance Industry

The Impact of Artificial Intelligence AI in Insurance

Artificial intelligence is changing the world in the best way possible. It began to take over several sectors, including insurance. The insurance and finance industry has brought radical transformation since the evolution and use of AI in insurance underwriting.

AI is altering conventional insurance operations and methods significantly. Due to the high volume nature of insurance firm activities, AI technology can indeed have a digital transformation effect.

Some of the insurance companies use Artificial Intelligence (AI) and machine learning (ML) technologies to automate certain stages of the claims management process and improve customer service. Furthermore, blockchain technology is being used for fraud detection.

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How Does AI Impacts Insurance Industry?

If you are an insurance and finance service provider, you must have to know about Artificial intelligence in insurance use cases and the impact of AI on the future of insurance services.

Here, we have compiled a list of top AI applications in insurance and the adoption of this Artificial intelligence in insurance use cases will bring automation to processes, optimize productivity, and aid insurers in delivering top-notch digital experiences to their clients.

1. AI for Digital Insurance Claiming

Claim processing is a core function of insurers. It consumes lot of time of resources in manually verifying the documents.

AI for digital insurance claiming is one of the best applications of the technology. Unlike messy paperwork’s, AI insurance services digitally captures required information from smart copies and makes insurance claiming faster and efficient. Intelligent AI algorithms will take over and automate all the early claiming processes with more accuracy.

2. Risk Management Use Case in Insurance

Artificial intelligence insurance aids insurance, investment, and finance companies identify fraudulent claims and financial risks faster. AI apps or AI solutions build a secure platform between customers and insurance executives to make the process seamless.

AI and ML algorithms will verify customer documents and find risk profiles in minutes. By validating the information regarding credit, financial stability, and personal behaviour, AI-powered insurance apps help service providers’ possible insights into risks.

3. AI In Insurance Underwriting

The use of AI in insurance for automating the manual process of underwriting will save a lot of time of resources and improves productivity. Leveraging the capabilities of AI, ML, and deep learning, intelligent AI insurance app gathers massive data from various resources like external applications, insurers, and customers. Later, this information will be automatically categorized by in-built segregation tools and stored in respective records.

So, such an automated process will aid insurers in analysing claim type, customer risk profile, deciding the insurance eligibility, etc. Hence, AI in insurance underwriting is the best digital solution to save time, automate the underwriting process, and prevent manual errors in data.

4. Artificial intelligence Insurance for Budget Panning

Price is central to customer decision-making for creating new budget-friendly insurance plans. Artificial intelligence in insurance use cases for determining competitor plans and creating customer-friendly plans is gaining popularity.

AI and ML applications in insurance analyse data and insurance offers of rivals and offer new term plans for existing customers. Such friendly insurance offers also help brands to grab the attention of new customers seamlessly.

This is the future of AI in insurance. Not only these, but the impact of AI on the future of insurance will also be far-fetched and move to new heights that you cannot imagine. AI insurance companies will become a leader in the industry, deliver top-notch automated performance over traditional companies, and be a quick responders to people who are interested to claim long-lasting life insurance coverages and many more profitable schemes.

So, ready to become a leading AI insurance company and stand out from the list of companies that follows traditional insurance processes.

Also Read: How Corona virus epidemic effect AI innovation in the insurance industry?

How Can Insurers Prepare For AI-driven Future?

Insurance sector is fueled by the widespread adoption and integration of deep learning, automation, and external data ecosystems. While none of us forecast what insurance industry will look like in the coming decade i.e. 2030, carriers can now take importance like below to prepare for AI-driven future.

Distribution:
The insurance purchasing experience is faster with less involvement of the customers and insurance executives. AI algorithms create risk profiles that provide sufficient information about personal behavior, thus reducing the cycle time to minutes or seconds to complete an auto, life policy or commercial insurance purchase.

Underwriting and Pricing:  
By 2030, manual warranty/ underwriting for personal and small-business products will cease to exist throughout accident, life and property insurance. With a combination of deep learning and machine learning models built into the technical stock a large percentage of the underwriting is automated and supported so the underwriting process is reduced to a few seconds.

Pricing:
Price is central to customer decision making, while carriers innovate to reduce competition entirely on price. Advanced ownership platforms connect insurance companies and customers and provide customers with different aspects of the industry like value, features and experiences.

Price competition Exacerbates and razor-thin margins are the norm in some segments. In other segments, specialized insurance offers allow differentiation and margin expansion. Within the jurisdiction that embraces change, the pace of price innovation will be faster.

Pricing is based on data-rich risk, consumption, dynamic, and empowers people to make decisions about how their actions affect insurance, coverage, and most importantly, pricing.

Claims: 
Claims processing will remain the primary task of carriers in 2030, but the head count partnered with claims will be minimized by 70% to 90% compared to 2018 levels. Advanced AI and ML algorithms increase the efficiency, accuracy, and routing of initial claims.

Claims for small-business insurance and personal lines are increasingly automated, allowing carriers to gain direct processing rates of over 90% and dramatically reducing claim processing time from days to minutes or hours depending on the task.

Also Read: How the Corona virus epidemic effect AI innovation in the insurance industry?

How Can Insurers Prepare For AI-driven Future?

Insurance sector is fuelled by the widespread adoption and integration of deep learning, automation, and external data ecosystems. While none of us forecast what insurance industry will look like in the coming decade i.e. 2030, carriers can now take importance like below to prepare for AI-driven future.

#1 Get Smart On AI-related Trends and Technologies

Although tectonic changes in the industry are tech-focused, addressing them is not at all the job of the IT team. Instead, board members and customer experience teams should invest resources and time to gain an in-depth understanding of technologies like AI and ML.

Also, insurers should understand market dynamics and discover smart solutions to address changing insurance needs. You should create plans that elevate your brand and your services on a global scale.

#2 Develop & Implement A Coherent Strategic Plan

Insurers need to develop a perspective on the areas in which they want to invest in AI to meet the business requirements and beat the competition. For example, AI is best suitable for automating processes, creating new connected infrastructure, or augmenting and modifying internal strategic capabilities.

#3 Create & Implement A Comprehensive Data Strategy

AI technologies work best in processing data and deriving results-driven insights. These insights will assist companies in creating data strategies that optimize the operational flow.

 #4 Create A Good Talent & Technical Infrastructure

The insurance company of the future needs talent with the right mind and skills. The front-line insurance workers of the next decade will be in high demand and must be willing to work in a dynamic mix of creativity, technology, and constantly evolving machine-supported and semi-automated tasks.

AI insurance company needs to integrate the skills, technologies, and insights to provide unique customer interactions, and experiences and generate value from the AI use cases of the future.

Final Thoughts

Well, the future of AI in the insurance industry will be expected to leave tremendous growth opportunities to the service providers. And, future of AI in insurance is expected to draw unbelievable automation across the value chain of insurance processes as we discussed in this article.

AI Insurance Services is ready to disrupt the insurance field like never before for both customers and insurers. Artificial intelligence insurance provides seamless user experiences at affordable rates and erases the complex paperwork insurance processes in the coming years. Yes, we cannot imagine the impact of AI on the future of insurance.

Insurance firms need to save money and time by making their processes more effective and efficient. With AI-based solutions, the possibilities are endless, and only a matter of time before we can see and experience these improvements.

If you are looking for the best AI service provider in India, USM Business Systems in the USA and Europe countries to automate your insurance business.

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How Much Does It Cost To Develop A Car Rental App?

 Car Rental App Development Cost and Features:

In this digital age, on-demand mobile apps (Android/iOS) are gaining momentum across the global nations. Convenience in booking services and reliability in service delivery, service delivery apps such as food delivery apps, car rental apps cab booking apps, and package delivery apps are in traction.

Yes. Service delivery apps are becoming popular across diverse industries and car rental is one of them. Today, through this article, we would like to share a guide on how much it costs to create a car rental app like Ekar. Herein, will walk you through the must-have features of on-demand car rental app development and the factors that impact the mobile application development costs.

Firstly, let’s take a look at the benefits of car rental apps to end-users.

What Is The Purpose Of The Car Rental App?

Renting a car has never been easier, but online car rental booking apps made this possible in minutes. Car rental apps are mobile apps or responsive web apps that help end-users avail of self-drive car services or driver-assisted care rental services on-demand. Offering a range of Cars and allowing users to toggle between fuel options, hassle-free payment options, and door delivery of booked cars, Car rental applications provide quick and affordable rentals to users.

Moreover, pay-per-hour, pay-per-day, and pay-per-month like plans, and car rental apps are offering users convenience in choosing from multiple rental options to meet their desired travel needs. Transparent pricing, free-parking slots finding facility, 24/7 customer support, GPS navigators, location-tracking facility, and flexibility to hire a car with or without fuel are all a few cool features that are creating waves for Car Rental Apps demand worldwide.

Must-Have Features To Add In A Car Rental App Development 

Features of a mobile application are core pillars that decide its success rate. Before commencing the development process, make sure to list the best features that deliver seamless experiences to users and derive great business value over time. Top mobile app development companies conduct thorough research on competitor apps, market opportunities, user preferences, and trends to create the best car rental app with the most useful features and functionalities.

Here are a few must-have features that you must integrate into a car rental app like Ekar development.

Must-have features of a Car Rental App development for users

• Quick registration
• Signup/login in simple steps
• Social media login feature
• User profile
• Multi-language feature
• Tracking available vehicles
• Location pinning
• In-app map integration
• Customized ride options
• Schedule pickups
• Two-way authentication
• Online payments
• Ride history
• Rebook vehicle
• Push notifications
• GPS navigators to track location in real-time and finding nearly parking slots

Features for driver module development of Car Rental Apps like Ekar Clone Development: 

• Signup and registration
• Driver profile management
• Bookings management (Pickup/Cancel/Door delivery of rented vehicle)
• In-app map integrations for tracking destinations
• Route optimization algorithms
• Rides history
• Payment history
• Push notifications

Must-Have Features To Develop Car Rental App Admin Module 

• User Profile Management
• Driver profile management
• Vehicles management
• Orders or bookings management
• Payments management
• Push notifications management
• Rental or ride pricing management
• Reviews and ratings management

The features and functionalities you selected will impact the final cost of a car rental apps. Ekar-like clone pap development needs certain features developed using Artificial Intelligence and IoT like cutting-edge technologies for ensuring remote monitoring, traffic monitoring, and driver behavior analytics, and vehicle performance monitoring etc. Adding such features will increase the development costs of car rental apps but take it to new heights.

Which Technology Stack Is Best for Car Rental App Development? 

The technology stack you select for car rental app development plays a significant role that impacts performance, application scalability, responsiveness, user experiences, security, and overall decides its success rate.   

• Best programming languages for Car Rental App development on Android OS- Kotlin, Java
• Most preferred programming languages for Car Rental App Development for iOS- Swift and Objective C
• Programming languages for Cross-platform Car Rental App development- Flutter, Xamarin, and React Native
• Frameworks used for Mobile App Development– Android for Android app development and iOS SDKs for iPhone app development
• Database: MongoDB
• Push Notifications: Twilio
• Storage: AWS and Google Cloud
• Payments: Third-party Payment Integrations

How Much Does It Cost For Car Rental App Development?

The cost to develop an Ekar-like car rental app development will range between $30,000 to $75,000. However, the development costs of a mobile app will depend on many factors such as features and functionalities, app development platform, and application type (native app development, cross-platform development, or hybrid application development.

Further, the cost of a car rental platform like Ekar will also depend on the tools and technologies used in the development process. Integrating car rental apps with advanced technologies, such as Artificial Intelligence will offer several benefits, such as dynamic pricing, AI chatbots, license recognition, etc. It will increase the development costs of the car rental platform beyond $100,000, but automate the processes, and improve the operational excellence and experiences.

Moreover, mobile app developers’ location and their team size are also significant cost-impacting factors that determine the cost of car rental app development like Ekar. For instance, top mobile app developers in the USA and European nations will charge approximately $100-$150/hr, which is very high compared to the best mobile app development companies in India like budget-friendly markets.

The car rental app development cost will also depend on the post-launch services, including app maintenance and support. If you introduce your MVP into the market, you can save money to an extent, however, you must incur certain costs for updating features as per the user needs.

Are you planning to invest in car rental app development and have an idea?

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Inspired by the paper-folding art of origami, engineers have discovered a way to make a single plastic cubed structure transform into more than 1,000 configurations using only three active motors.

Researchers have developed a self-powered 'bug' that can skim across the water, and they hope it will revolutionize aquatic robotics.

Researchers at Binghamton University, State University of New York have developed a self-powered "bug" that can skim across the water, and they hope it will revolutionize aquatic robotics.

Generative AI is a rapidly evolving field with a plethora of fascinating applications, from creating realistic images and videos to generating human-like text and beyond. As the technology advances, the demand for skilled professionals who can harness the power of generative AI is growing exponentially. However, navigating the myriad of tutorials and courses available can be overwhelming, especially when trying to acquire these critical skills quickly.

To help you on your journey, we have curated a list of some of the highest-quality courses from respected providers such as DeepLearning.ai, Google Cloud, AWS, IBM, and more. These courses are designed with a strong practical focus, ensuring that you gain real-world skills needed to build applications powered by large language models (LLMs). The best part? Most of these courses are available for free, making it easier than ever to dive into the world of generative AI.

In this article, we provide an overview of these top courses, highlighting their key features and content to help you find the best fit for your learning needs. Whether you’re a beginner just starting out or an advanced developer looking to deepen your expertise, there’s something here for everyone.

Here are the courses we cover:

1. Generative AI for Everyone by DeepLearning.ai
2. Introduction to Generative AI by Google Cloud
3. Generative AI: Introduction and Applications by IBM
4. ChatGPT Promt Engineering for Developers by OpenAI and DeepLearning.ai
5. LangChain for LLM Application Development by LangChain and DeepLearning.ai
6. LangChain: Chat with Your Data by LangChain and DeepLearning.ai
7. Open Source Models with Hugging Face by Hugging Face and DeepLearning.ai
8. Building LLM Powered Apps by Weights & Biases
9. Generative AI with Large Language Models by AWS and DeepLearning.ai
10. LLM University by Cohere
11. Amazon Bedrock & AWS Generative AI by AWS
12. Finetuning Large Language Models by Lamini and DeepLearning.ai
13. Reinforcement Learning from Human Feedback by Google Cloud and DeepLearning.ai
14. Generative AI for Software Development by DeepLearning.ai
15. Generative AI for Developers by Google Cloud

If this in-depth educational content is useful for you, subscribe to our AI mailing list to be alerted when we release new material. 

Top Generative AI Courses with Practical Focus

Now let’s have an overview of some of the top generative AI courses available today. These courses are designed to equip you with practical skills and knowledge to excel in the field of generative AI.

1. Generative AI for Everyone by DeepLearning.ai

Level: Beginner

Duration: 3 hours

Cost: Free

Instructor: Andrew Ng, founder of DeepLearning.ai, co-founder of Google Brain and Coursera

Audience: This course is tailored for anyone keen on understanding the applications, impacts, and foundational technologies of generative AI. No prior coding skills or AI knowledge are required, making it accessible to a broad audience.

Content:

• Introduction to Generative AI: An overview of what generative AI is and its capabilities.
• Applications and Limitations: Insights into what generative AI can and cannot do, helping learners set realistic expectations.
• Practical Uses: Guidance on integrating generative AI into various personal or business contexts.
• Debunking Myths: Addressing common misconceptions about generative AI and promoting a clear understanding.
• Best Practices: Strategies for effective learning and evaluating the potential usefulness of generative AI in different scenarios.

This concise yet comprehensive course offers a foundational understanding of generative AI, making it an excellent starting point for anyone looking to delve into this transformative technology.

2. Introduction to Generative AI by Google Cloud

Level: Beginner

Duration: Specialization with 4 courses (approximately 4 hours total)

Cost: Free

Instructor: Google Cloud Training Team

Audience: This course is ideal for individuals looking to deepen their understanding of generative AI and large language models. While it is beginner-friendly, a basic grasp of AI concepts will help learners fully absorb the material.

Content:

• Generative AI Fundamentals: Defining generative AI and explaining its underlying mechanisms.
• Applications of Generative AI: Exploring various real-world applications and use cases of generative AI.
• Large Language Models: Defining LLMs, their functionalities, and practical use cases.
• Prompt Tuning: An overview of prompt tuning and its significance in optimizing AI outputs.
• Google’s Gen AI Development Tools: Insight into the tools provided by Google for developing generative AI applications.
• Responsible AI Practices: Discussion on responsible AI practices and how Google implements its AI Principles to ensure ethical AI development.

While the course does have a notable focus on Google’s AI practices and tools, it remains a robust introduction to generative AI and LLMs, providing valuable knowledge and insights for anyone interested in the field.

3. Generative AI: Introduction and Applications by IBM

Level: Beginner

Duration: 6 hours

Cost: Free

Instructor: Rav Ahuja, Chief Curriculum Officer and Global Program Director at IBM Skills Network

Audience: This course is perfect for those seeking to understand generative AI with a strong emphasis on practical applications and real-world use cases. It is well-suited for individuals interested in learning about generative AI models and tools across various media formats, including text, code, image, audio, and video.

Content:

• Generative vs. Discriminative AI: Understanding the fundamental differences between generative and discriminative AI.
• Capabilities and Use Cases: Insight into the abilities of generative AI and its practical applications in the real world.
• Sector-Specific Applications: Exploration of how generative AI is applied across different industries and sectors.
• Generative AI Models and Tools: Detailed examination of common generative AI models and tools used for generating text, code, images, audio, and video.

This comprehensive course provides a broad understanding of generative AI, emphasizing its real-world applications and diverse use cases, making it an excellent resource for beginners aiming to grasp the practical aspects of this technology.

4. ChatGPT Promt Engineering for Developers by OpenAI and DeepLearning.ai

Level: Beginner

Duration: 1 hour

Cost: Free

Instructors: Isa Fulford, Member of Technical Staff at OpenAI, and Andrew Ng, founder of DeepLearning.ai, co-founder of Google Brain and Coursera

Audience: This course is designed for developers who are beginning to build applications based on large language models. Basic Python coding skills are recommended to fully benefit from the course content.

Content:

• Introduction into LLMs: An overview of how large language models work.
• Best Practices for Prompt Engineering: Guidance on creating effective prompts for various tasks.
• Using LLM APIs: Practical examples of using LLM APIs in applications for tasks such as:
  • Summarizing: Condensing user reviews for brevity.
  • Inferring: Performing sentiment classification and topic extraction.
  • Transforming Text: Executing tasks like translation, spelling, and grammar correction.
  • Expanding Text: Automatically generating content such as emails.
• Effective Prompt Writing: Two key principles for writing effective prompts and systematic approaches to engineering good prompts.
• Building a Custom Chatbot: Step-by-step instructions on building a custom chatbot.
• Hands-on Experience: Numerous examples and interactive exercises in a Jupyter notebook environment to practice prompt engineering.

This succinct course provides developers with the essential skills and knowledge to harness the power of LLMs in their applications, emphasizing practical examples and hands-on experience to ensure a solid understanding of prompt engineering.

5. LangChain for LLM Application Development by LangChain and DeepLearning.ai

Level: Beginner

Duration: 1 hour

Cost: Free

Instructors: Harrison Chase, co-founder and CEO at LangChain, and Andrew Ng, founder of DeepLearning.ai, co-founder of Google Brain and Coursera

Audience: This beginner-friendly course is designed for developers who want to learn how to expand the use cases and capabilities of language models in application development using the LangChain framework. Basic Python knowledge is recommended to maximize the course benefits.

Content:

• Models, Prompts, and Parsers: Learn how to call LLMs, provide effective prompts, and parse the responses.
• Memories for LLMs: Understand how to use memories to store conversations and manage limited context space, enhancing the functionality of your applications.
• Chains: Create sequences of operations to build more complex workflows and capabilities within your applications.
• Question Answering over Documents: Apply LLMs to your proprietary data and specific use case requirements, making your applications more versatile and powerful.
• Agents: Explore the emerging development of LLMs as reasoning agents, opening up new possibilities for advanced application functionalities.

This concise course equips developers with the skills to significantly expand the use cases and capabilities of language models using the LangChain framework, enabling the creation of robust and sophisticated applications in a short amount of time.

6. LangChain: Chat with Your Data by LangChain and DeepLearning.ai

Level: Beginner

Duration: 1 hour

Cost: Free

Instructor: Harrison Chase, co-founder and CEO at LangChain

Audience: This course is aimed at developers who want to learn how to build practical applications that interact with data using LangChain and LLMs. Developers should be familiar with Python.

Content:

• Retrieval Augmented Generation (RAG): Learn how to retrieve contextual documents from external datasets.
• Chatbot Development: Build a chatbot that answers questions based on your documents.
• Document Loading: Explore over 80 loaders to access various data sources, including audio and video.
• Document Splitting: Understand best practices for data splitting.
• Vector Stores and Embeddings: Discover embeddings and vector store integrations in LangChain.
• Advanced Retrieval: Master techniques for accessing and indexing data to retrieve relevant information.
• Question Answering: Create a one-pass question-answering solution.

This concise course provides developers with the skills to effectively use language models and LangChain, enabling the creation of powerful applications using their own data.

7. Open Source Models with Hugging Face by Hugging Face and DeepLearning.ai

Level: Beginner

Duration: 1 hour

Cost: Free

Instructors: Maria Khalusova, Marc Sun, and Younes Belkada from the Hugging Face technical team

Audience: This course is for anyone looking to quickly and easily build AI applications using open-source models.

Content:

• Model Selection: Choose open-source models from the Hugging Face Hub for NLP, audio, image, and multimodal tasks.
• Transformers Library: Learn to use the transformers library to create a chatbot capable of multi-turn conversations.
• NLP Tasks: Translate between languages, summarize documents, and measure text similarity for search and retrieval.
• Audio Tasks: Convert audio to text with Automatic Speech Recognition (ASR) and text to audio with Text-to-Speech (TTS).
• Multimodal Tasks: Generate audio narrations for images by combining object detection and text-to-speech models.

This course provides the essential building blocks to combine into pipelines, enabling you to develop AI-enabled applications using Hugging Face’s open-source models.

8. Building LLM-Powered Apps by Weights & Biases

Level: Intermediate

Duration: 2 hours of video content

Cost: Free

Instructors: Shreya Rajpal, creator of Guardrails AI; Anton Troynikov, co-founder of Chroma; Shahram Anver, co-creator of Rebuff

Audience: This course is designed for developers looking to build LLM applications. Intermediate Python experience is required, but no prior machine learning skills are needed.

Content:

• Fundamentals of AI-Powered Applications: Learn the basics of APIs, chains, and prompt engineering for building AI applications.
• Hands-On Application Development: Follow a step-by-step guide to build your own app, using a support automation bot for a software company as an example.
• Enhancing Your LLM App: Discover methods for improving your LLM-powered app through experimentation and evaluation.

This course equips developers with the necessary skills to create and optimize LLM applications, providing practical insights and hands-on experience.

9. Generative AI with Large Language Models by AWS and DeepLearning.ai

Level: Intermediate

Duration: 16 hours

Cost: Free

Instructors: Chris Fregly and Shelbee Eigenbrode, Principal Solutions Architects for Generative AI at Amazon Web Services (AWS), Antje Barth, Principal Developer Advocate for Generative AI at AWS, and Mike Chambers, Developer Advocate for Generative AI at AWS.

Audience: This course is for developers who want to understand the fundamentals of generative AI and how to deploy it in real-world applications. Intermediate Python coding skills and a basic understanding of machine learning concepts, such as supervised and unsupervised learning, loss functions, and data splitting, are required.

Content:

• Generative AI Lifecycle: Learn the key steps in a typical LLM-based generative AI lifecycle, from data gathering and model selection to performance evaluation and deployment.
• Transformer Architecture: Gain a detailed understanding of the transformer architecture powering LLMs, including their training process and how fine-tuning adapts them to specific use cases.
• Empirical Scaling Laws: Optimize the model’s objective function by balancing dataset size, compute budget, and inference requirements using empirical scaling laws.
• Advanced Techniques: Apply state-of-the-art methods for training, tuning, inference, and deployment to maximize model performance within project constraints.
• Business Implications: Explore the challenges and opportunities generative AI presents for businesses through insights from industry researchers and practitioners.

This comprehensive course provides developers with the knowledge and tools to effectively deploy generative AI in real-world applications, emphasizing practical techniques and industry insights.

10. LLM University by Cohere

Level: Intermediate to Advanced

Duration: 8 modules consisting of 42 articles, with content available in both video and text formats

Cost: Free

Instructors: Cohere team

Audience: This course is designed for developers and technical professionals who want to quickly and efficiently start building LLM applications.

Content:

• Key Concepts of Large Language Models: Gain a deep understanding of the fundamental concepts behind LLMs.
• Text Representation and Generation: Learn the principles of text representation and how LLMs generate text.
• Deployment: Discover how to deploy LLM applications using various tools.
• Semantic Search: Explore how semantic search works.
• Prompt Engineering: Understand the techniques of prompt engineering.
• Retrieval-Augmented Generation (RAG): Learn how to implement RAG in your applications.
• Tool Use: Get hands-on experience with various tools essential for LLM development.

This comprehensive course provides a thorough grounding in both basic and advanced concepts, enabling developers to understand the inner workings of LLMs and build sophisticated applications.

11. Amazon Bedrock & AWS Generative AI by AWS 

Level: Beginner to Advanced

Duration: 11 hours

Cost: $19.99

Instructor: Rahul Trisal, AWS Community Builder in the Serverless Category and Senior AWS Architect with over 15 years of experience in AWS Cloud Strategy, Architecture, and Migration

Audience: This course is aimed at developers who want to build LLM applications using AWS infrastructure. Basic AWS knowledge is recommended, but the course includes a refresher on Python, AWS Lambda, and API Gateway for those who need it.

Content:

• Introduction to AI/ML: Basic overview of AI/ML concepts.
• Generative AI Fundamentals: Learn how generative AI works and explore foundation models in depth.
• Amazon Bedrock: Detailed console walkthrough, architecture, pricing, and inference parameters.
• Use Cases: Seven practical applications including design, text summarization, chatbots, code generation, and more.
• GenAI Project Lifecycle: Comprehensive guide on defining use cases, choosing a foundation model, prompt engineering, and fine-tuning models.

This course provides a thorough introduction to building LLM applications on AWS, covering both foundational concepts and practical implementations to equip developers with the necessary skills and knowledge.

12. Finetuning Large Language Models by Lamini and DeepLearning.ai

Level: Intermediate

Duration: 1 hour

Cost: Free

Instructor: Sharon Zhou, Co-Founder and CEO of Lamini

Audience: This course is designed for learners who want to understand the techniques and applications of finetuning large language models. Familiarity with Python and a deep learning framework such as PyTorch is recommended.

Content:

• Application of Finetuning: Learn when and why to apply finetuning on LLMs.
• Data Preparation: Understand how to prepare your data for finetuning.
• Training and Evaluation: Gain hands-on experience training and evaluating an LLM on your data.

Upon completion, learners will be equipped with the skills to effectively finetune LLMs, enhancing their ability to tailor models to specific applications and datasets.

13. Reinforcement Learning from Human Feedback by Google Cloud and DeepLearning.ai

Level: Intermediate

Duration: 1 hour

Cost: Free

Instructor: Nikita Namjoshi, Developer Advocate at Google Cloud

Audience: This course is for anyone with intermediate Python knowledge interested in learning about using the Reinforcement Learning from Human Feedback (RLHF) technique.

Content:

• Conceptual Understanding of RLHF: Gain insights into the RLHF training process.
• Datasets Exploration: Learn about the “preference” and “prompt” datasets used in RLHF training.
• Practical Application: Use the open-source Google Cloud Pipeline Components Library to fine-tune the Llama 2 model with RLHF.
• Model Assessment: Compare the tuned LLM against the original base model by evaluating loss curves and using the “Side-by-Side (SxS)” method.

This course equips learners with the conceptual and practical skills needed to apply RLHF for tuning LLMs, enhancing their understanding and capabilities in this advanced technique.

14. Generative AI for Software Development by DeepLearning.ai

Level: Intermediate

Duration: 3 courses (around 15 hours), starting on Sep 25, 2024

Cost: Free

Instructor: Laurence Moroney, Chief AI Scientist at VisionWorks Studios and former AI lead at Google

Audience: This course is designed for software developers who want to explore how to use LLMs to improve their efficiency and optimize their code quality.

Content:

• Understanding LLMs: Learn how large language models work to effectively leverage them in your development process.
• Pair-Coding with LLMs: Modify data structures for production and handle big data scales efficiently with the assistance of an LLM.
• Software Testing with LLMs: Use LLMs to identify bugs, create edge case tests, and update code to correct errors, enhancing your software testing processes.
• Database Implementation and Design: Build a local database from scratch and partner with an LLM to optimize software design for efficient and secure data access.

This comprehensive course equips software developers with the knowledge and skills to integrate LLMs into their workflow, enhancing productivity and code quality.

15. Generative AI for Developers by Google Cloud

Level: Intermediate to Advanced

Duration: 11 courses (about 19 hours in total)

Cost: Free

Instructor: Google Cloud team

Audience: This Generative AI Learning Path is tailored for App Developers, Machine Learning Engineers, and Data Scientists. It’s recommended to complete the Introduction to Generative AI learning path before starting this course.

Content:

• Generative AI Applications: Explore various applications, including image generation, image captioning, and text generation.
• Gen AI Model Architectures: Dive deep into model architectures such as the attention mechanism, encoder-decoder architecture, and transformer models.
• Vertex AI Studio: Learn how to use Vertex AI Studio for developing and deploying generative AI models.
• Responsible AI for Developers: Understand the principles of responsible AI and how to implement them in your projects.
• Machine Learning Operations (MLOps) for Generative AI: Gain insights into MLOps practices tailored for generative AI workflows.

Although the course emphasizes Google Cloud infrastructure and practices, it offers a comprehensive understanding of how generative AI works and how to apply these models in real-world scenarios.

Elevate Your Development Skills with Generative AI Courses

As generative AI continues to revolutionize the tech landscape, developers must equip themselves with the latest skills to stay competitive. The courses outlined in this article provide targeted, practical training in generative AI, helping you build sophisticated LLM-powered applications. Featuring instruction from esteemed providers such as DeepLearning.ai, Google Cloud, AWS, and IBM, these courses ensure you gain the expertise needed to thrive in this fast-evolving field.

Whether you’re a beginner ready to start your journey or an experienced developer seeking to enhance your capabilities, these courses offer a clear pathway to mastering generative AI. Embrace these learning opportunities and take your development skills to the next level with confidence and competence.

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The post Accelerate Your AI Skills: Essential Generative AI Courses for Developers appeared first on TOPBOTS.

Inspired by the paper-folding art of origami, North Carolina State University engineers have discovered a way to make a single plastic cubed structure transform into more than 1,000 configurations using only three active motors. The findings could pave the way for shape-shifting artificial systems that can take on multiple functions and even carry a load—like versatile robotic structures used in space, for example.

ChatGPT-Maker Releases New Bargain Version

OpenAI has released a new chatbot that’s almost as good as its flagship AI engine — ChatGPT 4o — and much cheaper to run.

Dubbed “ChatGPT 4o Mini,” the new AI engine is free-to-use on a limited basis to anyone visiting the ChatGPT Web site.

ChatGPT 4o Mini is expected to be a hit with developers looking to build AI applications atop the AI engine, which OpenAI says costs 60% less to run.

An important note: While ChatGPT 4o Mini is less advanced as the OpenAI flagship version, it’s still plenty smart.

ChatGPT 4o Mini, for example, beats-out the original AI software that powered ChatGPT to world fame and frenzy in late 2022, according to OpenAI test reports.

In other news and analysis on AI writing:

*In-Depth Guide: 10 Best AI SEO Tools: Writers looking for a nice round-up of AI-powered tools specializing in search engine optimization may want to check-out this piece.

The guide offers a short-and-sweet summary of ten AI-powered SEO tools that writer Antoine Tardif considers tops.

Observes Tardif: “By leveraging these technologies, you can streamline your SEO efforts, produce high-quality content and improve your website’s visibility and user experience.”

*The MVP of AI Chatbots?: Facebook Founder Takes Another Swing for the Fences: Longtime AI evangelist Mark Zuckerberg has updated his challenge to ChatGPT, dubbed, Llama 3.1.

Observes writer Anuj Mudaliar: “While both models (AI engines) are thought to exhibit excellent performance in natural language processing, Llama 3.1’s relatively smaller parameter size may limit its ability to complete complex tasks, as GPT-4 works on 1.76 trillion parameters.

“However, practical performance is yet to be measured by users on a wide scale.”

*Très magnifique?: French AI Startup Says It’s Built a Better ChatGPT: French AI startup Mistral is out with its own competitor to ChatGPT, which it says matches — and sometimes exceeds — the market leader’s performance.

For example: Mistral’s ability to auto-generate accurate computer code is actually better than the most robust version of ChatGPT — ChatGPT 4o — according to the company.

Dubbed Mistral Large 2, the new AI engine is available on Google Vertex AI, Azure AI Studio, Amazon Bedrock and IBM watsonx.ai.

*Scribblers Rejoice!: Microsoft Promising to Transform Chicken Scratch Into Digital Gold: Users of MS Copilot in OneNote may soon have access to a tool that enables input into OneNote via handwritten stylus.

The overall goal is for MS Copilot to ingest the handwritten notes and then enable users to auto-generate written summaries, ask questions of the data they’ve entered and auto-generate to-do lists based on the notes.

Currently, the new tool is in beta testing.

*Can We Talk?: When Study Data Becomes a Conversationalist: Research software firm Recollective is out with a new AI tool that offers conversational access to qualitative research.

Observes Alfred Jay, CEO, Recollective: “Our new AI features are designed to complement and enhance the way researchers work, enabling them to focus on what truly matters: extracting actionable insights and creating compelling narratives.”

Specifically, researchers can pose targeted questions to the study data they’ve gathered and engage in a dialog with the research to unveil insights and trends they may have otherwise missed.

*Humanizey AI Hawks Solution to Bot-Babble: Writers looking for a more ‘human feel’ from writing auto-generated by AI may want to give AI Humanizer a test-drive.

The tool is designed to auto-rewrite text produced by AI chatbots so that it sounds more human.

Plus, the resulting, re-written text also should bypass detection as ‘AI generated’ when assessed by AI writing detectors such as GPTZero, Turnitin and Originality AI, according to David Holand, CEO, Humanizey AI.

*Another AI News Anchor Pops-Up: Because Humans Are So Yesterday: Add South Korean cable TV channel MBN to the growing list of news outlets using AI-powered news anchors to present the news.

This one is actually a knock-off of a human news anchor on the channel — Kim Ju-ha — and is programmed to look exactly like Ju-ha and mimic the female news anchor mannerisms.

Observes the AI bot, dubbed Al Kim: “I was created through deep learning 10 hours of video of Kim Ju-ha, learning the details of her voice, the way she talks, facial expressions, the way her lips move, and the way she moves her body.

“I am able to report news exactly the way that anchor Kim Ju-ha would.”

*Going for Google’s Jugular: ChatGPT-Maker Tinkers With New Search Engine: OpenAI is currently testing an AI-powered search engine it hopes will unseat Google as the King of Search.

Observes writer Deepa Seetharaman: “The tool, called SearchGPT, will summarize the information found on Web sites, including news sites and let users ask follow-up questions — just as they can currently with OpenAI’s popular chatbot, ChatGPT.

“SearchGPT is OpenAI’s most direct challenge yet to Google’s dominance in search since the release of ChatGPT in 2022 caught the tech company flat-footed.”

*Fast Times at AI High: New Startup Looking to Build ‘AI-First’ Schools: Former OpenAI researcher Andrej Karpathy is looking to redefine education by building new schools with AI at their core.

Karpathy describes his new venture, dubbed ‘Eureka Labs,’ as a “new kind of school that is AI native,” with the express aim of developing a “Teacher + AI symbiosis” that will allow “anyone to learn anything,” according to writer Andrew Tarantola.

Karpathy “envisions an education system built from the ground-up with AI as its core tenet — with human teachers developing lesson plans while being supplemented in the classroom by digital assistants,” Tarantola adds.

Share a Link:  Please consider sharing a link to https://RobotWritersAI.com from your blog, social media post, publication or emails. More links leading to RobotWritersAI.com helps everyone interested in AI-generated writing.

–Joe Dysart is editor of RobotWritersAI.com and a tech journalist with 20+ years experience. His work has appeared in 150+ publications, including The New York Times and the Financial Times of London.

The post Dumber, Cheaper appeared first on Robot Writers AI.

While interest in Siggraph as an event has declined over the years, the advent of generative AI and the near-term potential arrival of AGI (Artificial General Intelligence) should cause a significant resurgence. This is because AI dramatically changes how people […]

The post Siggraph 2024: Learning About the Present and Future of Generative AI and the Coming of AGI appeared first on TechSpective.

Scientists have identified an automatic behavior in flies that helps them assess wind conditions -- its presence and direction -- before deploying a strategy to follow a scent to its source. The fact that they can do this is surprising -- can you tell if there's a gentle breeze if you stick your head out of a moving car? Flies aren't just reacting to an odor with a preprogrammed response: they are responding in context-appropriate manner. This knowledge potentially could be applied to train more sophisticated algorithms for scent-detecting drones to find the source of chemical leaks.

Researchers create a storybook generation system for personalized vocabulary learning.