Team Mar 27, 2023 No Comments
The never-ending rumors of OpenAI bringing out GPT-4 finally ended last week when the Microsoft-backed company released the much-awaited model. GPT-4 is being hailed as the company’s most advanced system yet and it promises to provide safer and more useful responses to its users. For now, GPT-4 is available on ChatGPT Plus and as an API for developers.
The newly launched GPT-4 can generate text and accept both image and text inputs. As per OpenAI, GPT-4 has been designed to perform at a level that can be compared to humans across several professional and academic benchmarks. The new ChatGPT-powered Bing runs on GPT-4. GPT-4 has been integrated with Duolingo, Khan Academy, Morgan Stanley, and Stripe, OpenAI added.
This announcement follows the success of ChatGPT, which became the fastest-growing consumer application in history just four months ago. During the developer live stream, Greg Brockman, President and Co-Founder of OpenAI Developer Livestream that OpenAI has been building GPT-4 since they opened the company.
OpenAI also mentioned that a lot of work still has to be done. The company is looking forward to improving the model “through the collective efforts of the community building on top of, exploring, and contributing to the model.”
So, what makes GPT-4 stand out from its predecessors? Let us find out:
One of the biggest upgrades for GPT-4 has been its multimodal abilities. This means that the model can process both text and image inputs seamlessly.
As per OpenAI, GPT-4 can interpret and comprehend images just like text prompts. Any specific type or image size does not bind this feature. The model can understand and process all kinds of images- from a hand-drawn sketch, a document containing text and images, or a screenshot.
OpenAI assessed the performance of GPT-4 on traditional benchmarks created for machine learning models. The findings have shown that GPT-4 surpasses existing large language models and even outperforms most state-of-the-art models.
As many ML benchmarks are written in English, OpenAI sought to evaluate GPT -4’s performance in other languages too. OpenAI informs that it used Azure Translate to translate the MMLU benchmark.
Image: OpenAI
OpenAI mentions that in 24 out of 26 languages tested, GPT-4 surpassed the English-language performance of GPT-3.5 and other large language models like Chinchilla and PaLM, including for low-resource languages like Latvian, Welsh, and Swahili.
To differentiate between the capabilities of GPT-4 and GPT-3.5, OpenAI conducted multiple benchmark tests, including simulating exams originally meant for human test-takers. The company utilized publicly available tests like Olympiads and AP free response questions and also obtained the 2022-2023 editions of practice exams. We did not provide any specific training for these tests.
Here are the results:
Image Source: OpenAI
OpenAI dedicated six months to enhancing GPT-4’s safety and alignment with the company’s policies. Here is what it came up with:
1. According to OpenAI, GPT-4 is 82% less likely to generate inappropriate or disallowed content in response to requests.
2. It is 29% more likely to respond to sensitive requests in a way that aligns with the company’s policies.
3. It is 40% more likely to provide factual responses compared to GPT-3.5.
OpenAI also mentioned that GPT-4 is not “infallible” and can “hallucinate.” It becomes incredibly important to not blindly rely on it.
OpenAI has been at the forefront of natural language processing advancements, starting with their GPT-1 language model in 2018. GPT-2 came in 2019. It was considered state-of-the-art at the time.
In 2020, OpenAI released its latest model, GPT-3 which was trained on a larger text dataset. It led to improved performance. Finally, ChatGPT came out a few months back.
Generative Pre-trained Transformers (GPT) are learning models that can produce text with a human-like capability. These models have a wide range of applications, including answering queries, creating summaries, translating text to various languages (even low-resource ones), generating code, and producing various types of content like blog posts, articles, and social media posts.
Team Mar 25, 2023 No Comments
Staying up-to-date with the latest happenings in data science is crucial due to the field’s rapid growth and constant innovation. Beyond conventional ways to stay updated and get information, podcasts can be a fun and convenient way to access expert insights and fresh perspectives. They can also provide crucial information to help you break into a data science career or advance it successfully.
Here is a list of some popular podcasts any data enthusiast cannot afford to miss this year.
Michael Helbling, Tim Wilson, and Moe Kiss are co-hosts who discuss various data-related topics. Lighthearted in nature, the podcast covers a wide range of topics, such as statistical analysis, data visualization, and data management.
Professor Margot Gerritsen from Stanford University and Cindy Orozco from Cerebras Systems are the hosts, and it features interviews with leading women in data science. The podcast explores the work, advice, and lessons learned by them to understand how data science is being applied in various fields.
Launched in 2014 by Kyle Polich, a data scientist, this podcast explores various topics within the field of data science. The podcast covers machine learning, statistics, and artificial intelligence, offering insights and discussions.
Not So Standard Deviations is a podcast hosted by Hillary Parker and Roger Peng. The podcast primarily talks about the latest advancements in data science and analytics. Staying informed about recent developments is essential to survive in this industry, and the podcast aims to provide insights that will help listeners to do that easily. By remaining up-to-date with the latest trends and innovations, listeners can be in a better position to be successful in this field.
Hosted by Xiao-Li Meng and Liberty Vittert, the podcast discusses news, policy, and business “through the lens of data science,” Each episode is a case study of how data is used to lead, mislead, and manipulate, adds the podcast.
Data Stories hosted by Enrico Bertini and Moritz Stefaner is a popular podcast exploring data visualization, data analysis, and data science. The podcast features a range of guests who are experts in their respective fields and discuss a wide variety of data-related topics, including the latest trends in data visualization and data storytelling techniques.
Felipe Flores, a data science professional with around 20 years of experience hosts this podcast. It features interviews with some of the top data practitioners globally.
Dr.Francesco Gadaleta hosts this podcast. It provides the latest and most relevant findings in machine learning and artificial intelligence, interviewing researchers and influential scientists in the field. Dr. Gadaleta hosts the show on solo episodes and interviews some of the most influential figures in the field.
Making Data Simple is a podcast that is hosted by AL Martin, the IBM VP of Data and AI development. The podcast talks about the latest developments in AI, Big data, and data science and their impact on companies worldwide.
Hosted by Emily Robinson and Jacqueline Nolis, the podcast provides all the knowledge needed to succeed as a data scientist. As per the website, the Build a Career in Data Science podcast teaches professionals diverse topics, from how to find their first job in data science to the lifecycle of a data science project and how to become a manager, among others.
Discretion: The list is in no particular order.
Team Mar 21, 2023 No Comments
A mere century ago, no one could have imagined we would be so reliant on technology. But, here we are, constantly being introduced to some of the smartest, trendiest, and mind-boggling automation procedures. The modern world will come to a screeching halt without the intervention of up-to-the-minute software, framework, and tools. TinyML is the new addition to the category of up-to-date technologies and telecommunications.
There are very few authentic resources available that put light on TinyML. TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers, authored by Daniel Situnayake and Pete Warden, is a prestigious and reliable source that answers the question: ‘what is TinyML?’. TinyML is an advancing field that combines Machine Learning and Embedded Systems to carry out quick instructions on limited memory and low-power microcomputers.
Another important feature of TinyML – the only machine learning framework it supports is TensorFlow Lite. Not sure what TensorFlow is? Check the detailed guide on TensorFlow .
Waiting long for machine learning magic is not a pleasant experience in every situation. When regular Machine Learning comes across commands like ‘Okay Google’, ‘Hey Siri’, or ‘Alexa’, the response can be time-intensive. But, the goal is quick responses from small directions like these. The desired fast reaction is only possible when the TinyML application is in effect.
It’s time to dive deep into the discussion of TinyML:
What is TinyML?
TinyML is a specialized study of Machine Learning that sits in the middle of embedded systems and machine learning (ML). It enables expansion and disposition of complex ML models on low-power processors that have limited computational abilities and memory.
TinyML allows electronic accessories to overcome their shortcomings by gathering information about the surroundings and functioning as per the data collected by ML algorithms. It also enables users to enjoy the benefits of AI in embedded tools. The simplest answer to ‘what is TinyML?’: TinyML is a framework to safely transfer knowledge and intelligence in electronic devices using minimal power.
The rapid growth in the software and hardware ecosystems enables TinyML application in low-powered systems (sensors). It warrants a real-time response, which is highly in demand in recent times.
The reason behind the growing popularity of TinyML in the real world is its ability to function perfectly fine without necessitating a strong internet connection, and massive monetary and time investment. It is rightly labeled as a breakthrough in the ML and AI industry.
TinyML has successfully addressed the shortcomings of standard Machine Learning (ML) models. The usual ML system cannot perform its best without entailing massive processing power. The newest version of ML is ready with its superpower to take over the industry of edge devices. It does not disappoint by demanding manual intervention such as connecting the tool to a charging point just to process simple commands or perform small tasks.
The application enables the prompt performance of minute but integral functions while eliminating massive power usage. A father figure in the TinyML industry, Pete Warden says, TinyML applications should not necessitate more than 1 mW to function.
If you are not well-versed in the basic concept of machine learning, our blog might help you understand it better.
New-age data processing tools and practices (Data Analytics, Data Engineering, Data Visualization, Data Modeling) have become mainstream due to their ability to offer instant solutions and feedback.
TinyML is solely based on data computing ability; it’s just faster than others. Here are a few uses of TinyML that we all are familiar with but probably, were not aware of the technology behind these:
Usually, a user anticipates an instant answer or reaction from a system/device when a command is stated. But a thorough process involving the transmission of instructions to the server and device capacitates the outcome. As one can easily fathom that this long process is time-consuming and thus the response gets delayed sometimes.
TinyML application makes the entire function simple and fast. Users are only concerned with the response; what goes inside does not pique the interest of many. Modern electronic gadgets that come with an integrated data processor are a boon of TinyML. It encourages the fast reaction that customers are fond of.
The exhaustive system of data management, transmission, and concocting can be intense. It also accelerates the risk of data theft or leak. TinyML safeguards user information to a great extent. How? The framework allows data processing in the device. The growing popularity of Data Engineering has also skyrocketed the need for safe data processing. From an entirely cloud-based data processing system to localized data processing, data leak is not a common problem for users anymore. TinyML erases the need to secure the complete network. You can now get away with just a secured IoT device.
A comprehensive server infrastructure is an ultimate foundation to ensure safe data transfer. As TinyML reduces the need for data transmission, the tools also consume less energy compared to the models manufactured before the popularity of the field. The common instances where TinyML is in use are microcontrollers. The low-power hardware uses minimal electricity to perform its duties. Users can go away for hours or days without changing batteries, even when they are in use for an extended period.
Regular operations using ML demand a strong internet connection. But, not anymore when TinyML is in action. The sensitive sensors seize information even without an internet connection. Thus, no need to worry about data delivery to the server without your knowledge.
Though it’s almost perfect, but not free from flaws. When the world is fascinated by the potential of TinyML and constantly seeking answers to ‘what is TinyML?’; it’s important to keep everyone informed of the challenges the framework throws at users. Combing through the internet and expert views, a few limitations of TinyML have been listed here:
Regular ML models use a certain amount of power that industry experts can predict. But TinyML does not leverage this advantage as each model/device uses different amounts of electricity. Thus, forecasting an accurate number is not possible. Another challenge users often face is an inability to determine how fast they can expect the outcome of commands on their device.
The small size of the framework also limits the memory storage space. Standard ML models weed out such complications.
The current retail chains manually monitor the stocks. The precision and accuracy of state-of-the-art technologies (such as TinyML) deliver better results compared to human expertise. Tracking inventories becomes straightforward when tinyML is in action. The introduction of footfall analytics and TinyML has transformed the retail business.
TinyML can be a game-changer for the farming industry. Whether it’s a survey of the health of farm animals or sustainable crop production, the possibilities are endless when the latest technologies are combined and adopted. 
The smart framework expedites factory production by notifying workers about necessary preventative maintenance. It streamlines manufacturing projects by implementing real-time decisions. It makes this possible by thoroughly studying the condition of the equipment. Quick and effective business decisions become effortless for this sector.
TinyML application simplifies real-time information collection, routing, and rerouting of traffic. It also enables fast movement of emergency vehicles. Ensure pedestrian safety and reduce vehicular emissions by combining TinyML with standard traffic control systems.
Experts believe we have a long way to go before we can claim TinyML as a revolutionary innovation. However, the application has already proved its ability and efficiency in the machine learning and data science industry. With an answer to the question ‘what is TinyML?’, we can expect the field to advance and the community to grow. The day is not far away when we will witness the application’s diverse implementation that none has envisaged. TinyML is ready to go mainstream with the expansion of supportive programming tools.
If you are someone with immense interest in the AI, ML, and DL industry, our courses might uncover new horizons and job opportunities for you. Check the website of Ivy Professional School to enroll in our training programs!
Team Mar 15, 2023 No Comments
Finally, GPT-4 is here – It can now take images, videos, and text inputs and generate responses.
The wait for the much anticipated GPT-4 is over.
Microsoft-backed OpenAI has revealed the launch of its highly anticipated GPT-4 model. It is being hailed as the company’s most advanced system yet, promising safer and more useful responses to its users. Fo
