Top Generative AI Tools 2024
Technique Improves The Reasoning Capabilities Of Large Language Models
CAMBRIDGE, MA - Large language models like those that power ChatGPT have shown impressive performance on tasks like drafting legal briefs, analyzing the sentiment of customer reviews, or translating documents into different languages.
These machine-learning models typically use only natural language to process information and answer queries, which can make it difficult for them to perform tasks that require numerical or symbolic reasoning.
For instance, a large language model might be able to memorize and recite a list of recent U.S. Presidents and their birthdays, but that same model could fail if asked the question "Which U.S. Presidents elected after 1950 were born on a Wednesday?" (The answer is Jimmy Carter.)
Researchers from MIT and elsewhere have proposed a new technique that enables large language models to solve natural language, math and data analysis, and symbolic reasoning tasks by generating programs.
Their approach, called natural language embedded programs (NLEPs), involves prompting a language model to create and execute a Python program to solve a user's query, and then output the solution as natural language.
They found that NLEPs enabled large language models to achieve higher accuracy on a wide range of reasoning tasks. The approach is also generalizable, which means one NLEP prompt can be reused for multiple tasks.
NLEPs also improve transparency, since a user could check the program to see exactly how the model reasoned about the query and fix the program if the model gave a wrong answer.
"We want AI to perform complex reasoning in a way that is transparent and trustworthy. There is still a long way to go, but we have shown that combining the capabilities of programming and natural language in large language models is a very good potential first step toward a future where people can fully understand and trust what is going on inside their AI model," says Hongyin Luo PhD '22, an MIT postdoc and co-lead author of a paper on NLEPs.
Luo is joined on the paper by co-lead authors Tianhua Zhang, a graduate student at the Chinese University of Hong Kong; and Jiaxin Ge, an undergraduate at Peking University; Yoon Kim, an assistant professor in MIT's Department of Electrical Engineering and Computer Science and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL); senior author James Glass, senior research scientist and head of the Spoken Language Systems Group in CSAIL; and others. The research will be presented at the Annual Conference of the North American Chapter of the Association for Computational Linguistics.
Problem-solving with programs
Many popular large language models work by predicting the next word, or token, given some natural language input. While models like GPT-4 can be used to write programs, they embed those programs within natural language, which can lead to errors in the program reasoning or results.
With NLEPs, the MIT researchers took the opposite approach. They prompt the model to generate a step-by-step program entirely in Python code, and then embed the necessary natural language inside the program.
An NLEP is a problem-solving template with four steps. First, the model calls the necessary packages, or functions, it will need to solve the task. Step two involves importing natural language representations of the knowledge the task requires (like a list of U.S. Presidents' birthdays). For step three, the model implements a function that calculates the answer. And for the final step, the model outputs the result as a line of natural language with an automatic data visualization, if needed.
"It is like a digital calculator that always gives you the correct computation result as long as the program is correct," Luo says.
The user can easily investigate the program and fix any errors in the code directly rather than needing to rerun the entire model to troubleshoot.
The approach also offers greater efficiency than some other methods. If a user has many similar questions, they can generate one core program and then replace certain variables without needing to run the model repeatedly.
To prompt the model to generate an NLEP, the researchers give it an overall instruction to write a Python program, provide two NLEP examples (one with math and one with natural language), and one test question.
"Usually, when people do this kind of few-shot prompting, they still have to design prompts for every task. We found that we can have one prompt for many tasks because it is not a prompt that teaches LLMs to solve one problem, but a prompt that teaches LLMs to solve many problems by writing a program," says Luo.
"Having language models reason with code unlocks many opportunities for tool use, output validation, more structured understanding into model's capabilities and way of thinking, and more," says Leonid Karlinsky, principal scientist at the MIT-IBM Watson AI Lab.
"No magic here"
NLEPs achieved greater than 90 percent accuracy when prompting GPT-4 to solve a range of symbolic reasoning tasks, like tracking shuffled objects or playing a game of 24, as well as instruction-following and text classification tasks. The researchers found that NLEPs even exhibited 30 percent greater accuracy than task-specific prompting methods. The method also showed improvements over open-source LLMs.
Along with boosting the accuracy of large language models, NLEPs could also improve data privacy. Since NLEP programs are run locally, sensitive user data do not need to be sent to a company like OpenAI or Google to be processed by a model.
In addition, NLEPs can enable small language models to perform better without the need to retrain a model for a certain task, which can be a costly process.
"There is no magic here. We do not have a more expensive or fancy language model. All we do is use program generation instead of natural language generation, and we can make it perform significantly better," Luo says.
However, an NLEP relies on the program generation capability of the model, so the technique does not work as well for smaller models which have been trained on limited datasets. In the future, the researchers plan to study methods that could make smaller language models generate more effective NLEPs. In addition, they want to investigate the impact of prompt variations on NLEPs to enhance the robustness of the model's reasoning processes.
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This research was supported, in part, by the Center for Perceptual and Interactive Intelligence of Hong Kong.
Qwen 2 Impressive LLM And AI Coding Assistant Can Help You Write The Perfect Code
Qwen-2 is an advanced open-source large language model and AI coding assistant that has shown significant improvements over its predecessor, Qwen 1.5. It is available in five different sizes and has been trained on multiple languages. The model excels in various benchmarks, including natural language understanding, coding, and mathematics, and is highly competitive with other leading models like GPT-4 and LLaMA 3.
AI Coding AssistantQwen-2, the latest iteration of the open-source large language model, represents a significant leap forward from its predecessor, Qwen 1.5. This advanced AI model is available in five sizes, ranging from 0.5 to 72 billion parameters, allowing users to select the optimal model based on their computational resources and specific needs. Qwen-2's versatility is further enhanced by its training on 27 additional languages beyond English and Chinese, making it a powerful tool for global applications.
One of the most impressive aspects of Qwen 2 is its outstanding performance across various benchmarks. The model ranks first on the open large language model leaderboard, showcasing its prowess in natural language understanding, coding, and mathematics. This top ranking underscores Qwen-2's robustness and reliability, making it a go-to choice for developers and researchers alike.
When compared to other leading models, Qwen2 holds its own, matching GPT-4 in overall performance and closely competing with LLaMA 3's 70 billion parameter model. This positions Qwen-2 as a formidable contender in the realm of advanced AI models, offering a compelling alternative to proprietary solutions.
Qwen-2's key capabilities include
The model's open-source availability on platforms like Hugging Face and ModelScope makes it accessible to a wide range of users, fostering collaboration and innovation in the AI community. The licensing flexibility, with models up to 57 billion parameters using the Apache 2.0 license and the 72 billion parameter model using the original Qwen license, caters to different usage and distribution requirements.
Unlocking Practical Applications with Qwen-2
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Qwen 2's advanced capabilities make it suitable for a variety of practical applications. In the realm of software development, the model's proficiency in generating Python code can significantly streamline the coding process, allowing developers to focus on higher-level tasks. This can lead to faster development cycles and improved code quality.
In the field of research and development, Qwen 2's ability to tackle complex mathematical problems can be a game-changer. By leveraging the model's computational power and problem-solving skills, researchers can accelerate their work and push the boundaries of their respective fields.
The convenience of installing Qwen 2 locally using LM Studio adds to its appeal, providing users with the flexibility and control to set up and use the model on their own hardware. This local installation option ensures data privacy and allows for customization to suit specific project requirements.
Extensive testing has demonstrated Qwen2's strong performance and reliability in generating Python code and solving logical and mathematical problems. This makes it a trustworthy tool for both academic and practical applications, giving users the confidence to rely on its outputs.
As the field of artificial intelligence continues to evolve, Qwen 2 stands out as a shining example of the potential for open-source models to drive innovation and democratize access to cutting-edge technology. With its impressive performance, versatility, and accessibility, Qwen-2 is poised to become a go-to resource for developers, researchers, and AI enthusiasts worldwide.
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Although just now emerging, gen AI certs and certificate programs can help you learn new skills and potentially establish new career opportunities. Here are 10 worth considering.
Generative AI is poised to disrupt nearly every industry, and IT professionals with highly sought after gen AI skills are in high demand, as companies seek to harness the technology for various digital and operational initiatives.
The State of Generative AI in the Enterprise report from Deloitte found that 75% of organizations expect generative AI technology to impact talent strategies within the next two years, and 32% of organizations that reported "very high" levels of generative AI expertise are already on course to make those changes.
With so much at stake, AI skills in general are earning significant premiums today, and generative AI is likely no different. If you want to learn more about generative AI skills and tools, while also demonstrating to employers that you have the skillset to tackle generative AI projects, here are 10 certifications and certificate programs to get your started.
Certified Generative AI Specialist (CGAI)Offered through the Chartered Institute of Professional Certifications, the Certified Generative AI specialist (CGAI) certification is designed to teach you the in-depth knowledge and skills required to be successful working with generative AI. The course covers principles of generative AI, data acquisition and preprocessing, neural network architectures, natural language processing, image and video generation, audio synthesis, and creative AI applications. Upon completing the learning modules, you will need to pass a chartered exam to earn the CGAI designation.
Cost: $490
Certified Generative AI ExpertThe Certified Generative AI Expert certification offered by Blockchain Council is aimed at anyone interested in gaining more expertise around generative AI. You'll be tested on your knowledge of generative models, neural networks, and advanced machine learning techniques. Modules include introduction to generative AI, generative AI for text, generative AI for images, generative AI for enterprises, generative AI for public services, data privacy in AI, prompt engineering for text analysis, and upcoming trends in generative AI. Through the self-paced certification program, you'll have the opportunity to gain hands-on experience with generative AI tools, and you'll earn a lifetime certification upon passing the final exam.
Cost: $249
Certified Prompt EngineerThe Certified Prompt Engineer certification offered by Blockchain Council is designed to validate your knowledge of foundational prompt engineering topics. The self-paced course covers prompt engineering in real-world case studies and gives you the opportunity to gain hands-on experience with the OpenAI API. Modules include introduction to prompt engineering, understanding prompts, principles of effective prompt engineering, creating effective prompts, working with OpenAI API, advanced prompt engineering, future of prompt engineering and AI conversations, and working with popular AI tools. The certification is best suited to AI researchers, software engineers, data scientists, and any tech professionals working with natural language processing.
Cost: $249
Databricks Generative AI Fundamentals CertificateDatabricks offers a Generative AI Fundamentals certificate program that helps you understand the basics of large language models (LLMs) and generative AI. The course consists of four self-placed videos that cover how generative AI technology is reshaping the industry, how it's being used for digital transformation, and it's impact on how businesses will be successful in the future. The videos include an introduction to the course, LLM applications, finding success with generative AI, and assessing the potential risks and challenges of AI. Upon completion, you will need to pass a knowledge test to earn a badge that you can display on your resume or LinkedIn profile.
Cost: $200
Generative AI Foundation CertificationThe Generative AI Foundation Certification from the Global Skills Development Council (GSDC) will validate that you have a foundational-level knowledge of generative AI and understand how it will impact technology environments. The foundational certification covers practical AI skills and knowledge, potential career prospects with AI, the industry's growing need for AI expertise, practical applications of AI, the business potential of AI, and how to foster AI talent and skills. There are an additional 10 paths for more advanced generative AI certification, including software development, business, cybersecurity, HR and L&D, finance and banking, marketing, retail, risk and compliance, prompt engineering, and project management.
Cost: $400
Generative AI LLMs Nvidia-Certified AssociateThe NVIDIA Generative AI LLMs certification is an associate-level credential that covers the foundational concepts for developing, integrating, and maintaining AI-driven applications using generative AI and LLMs with NVIDIA solutions. The exam covers topics such as fundamentals of machine learning and neural networks, prompt engineering, alignment, data analysis and visualization, experiment design, software development, Python libraries for LLMs, and LLM integration and deployment. Certification lasts for two years, but you can get recertified by retaking the exam, and upon passing you will receive a digital badge and optional certification that indicates your certification level and the topic.
Cost: $135
IBM Generative AI FundamentalsIBM's Generative AI Fundamentals Specialization program offered through Coursera is a self-paced, beginner-level course to get you up to speed on the fundamentals of generative AI. By the end of the program, you'll be able to explain the basic concepts, capabilities, models, tools, applications, and platforms of generative AI foundation models. You'll also gain a better understand of prompt engineering techniques, the limitations of generative AI, and how to use generative AI to implement improvements in the workplace. This specialization features five courses, including introduction and applications; prompt engineering basics; foundation models and platforms; impact, considerations, and ethical issues; and business transformation and career growth.
Cost: Free
Microsoft Azure AI Fundamentals: Generative AIThe Microsoft Azure AI Fundamentals: Generative AI training is a self-paced learning path to help you get started with generative AI. It's designed for entry-level candidates and beginners who want to better understand Azure OpenAI Service and prompt engineering. The course includes three modules that cover the fundamentals of generative AI, Azure OpenAI Service, and the principles surrounding the responsible use of generative AI.
Cost: Free
MIT Professional Education Applied Generative AI for Digital TransformationMIT Professional Education offers a three-week Applied Generative AI for Digital Transformation course, aimed at senior leaders, technology leaders, senior managers and mid-career executives, product marketers, and those working in customer experience. The self-paced course promises to develop your skills with generative AI tools, application of prompt engineering, automation of workflows using generative AI, employing generative AI in domain-specific tasks, and understanding and managing generative AI's organizational implications, both positive and negative. Upon completion of the course you will receive a certificate of completion and 4 MIT continuing education credit units.
Cost: $3,125
Purdue University Applied Generative AI SpecializationPurdue offers the Applied Generative AI Specialization program in partnership with Microsoft Azure, which it touts as a "cutting-edge program delving into Applied Generative AI." The 16-week online bootcamp program covers Python fundamentals, essentials of generative AI, prompt engineering, and ChatGPT, AI models and architecture, building LLM applications, image generation, and generative AI governance. You'll get access to more than 50 hours of core curriculum through live-online classes by industry experts, the chance to work on hands-on AI projects, attend live masterclasses taught by Purdue faculty and staff, and gain exposure to AI tools such as ChatGPT, OpenAI, and Dall-E. The program is designed for IT professionals, data analysts, business analysts, data scientists, software developers, analytics managers, and data engineers who want to learn more about generative AI.
Cost: $4,000
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