In the AI ecosystem, diffusion models are setting up the direction and pace of technological advancement. They are revolutionizing the way we approach complex generative AI tasks. These models are based on the mathematics of gaussian principles, variance, differential equations, and generative sequences. Modern AI-centric products and solutions developed by Nvidia, Google, Adobe, and OpenAI have put diffusion models at the center of the limelight. DALL.E 2, Stable Diffusion, and Midjourney are prominent examples of diffusion models that are making rounds on the internet recently.

Companies hire data and machine-learning professionals to help them with cutting-edge ML models. They spend often 80% of their time cleaning or dealing with data that is riddled with missing values, outliers, large load times, and a constantly changing schema. It is not uncommon for people to be far from their expectations. Data scientists may initially be enthusiastic to work on advanced models and insights, but this enthusiasm quickly fades amid daily schema changes, tables that stop updating, and other surprises that silently ruin models and dashboards. Although "data science" can be applied to many roles, such as product analytics or putting statistical models into production, there is one thing that is always true: data scientists, ML engineers, and data analysts often sit at the tail of the data pipeline.

Machine learning can be considered a component of artificial intelligence and involves training the machine to be more intelligent in its operations. AI technology focuses on incorporating human intelligence while machine learning is focused on making the machines learn faster. So we can say that machine learning engineers can provide faster and better optimizations to AI solutions. AI technology has had a massive impact on society and has transformed almost every industrial sector from planning to production. Thus machine learning engineers and experts are also of great value to this growing industry.

Machine Learning (ML) is the branch of Artificial Intelligence in which we use algorithms to learn from data provided to make predictions on unseen data. Recently, the demand for Machine Learning engineers has rapidly grown across healthcare, Finance, e-commerce, etc. According to Glassdoor, the median ML Engineer Salary is $131,290 per annum. In 2021, the global ML market was valued at $15.44 billion. It is expected to grow at a significant compound annual growth rate (CAGR) above 38% until 2029.

In 2029, the human race faces eradication and extinction by its own creation, a machine powered by a self-aware artificial intelligence (AI) program called Skynet. So goes the plot of The Terminator, Arnold Schwarzenneger's hit movie from the '80s. In the movie, surviving humans formed a resistance against Skynet and the machines. Their plan was to destroy the company that created the AI to prevent Skynet from being created in the first place. When the movie was released, the very concept that machines could be self-aware was a far-fetched idea and simply a figment of the writer's imagination.

You may have heard the news of ChatGPT fooling professors. Recently, it bamboozled scientists with convincing AI papers. The reports came from a preprint from the scientific bioRxiv server in December 2022. Researchers asked ChatGPT to create 50 abstracts based on several scientific sources. They found that medical researchers struggled to distinguish the fakes from the originals.

It's been almost three years since GPT-3 was introduced, back in May 2020. Since then, the AI text-generation model has garnered a lot of interest for its ability to create text that looks and sounds like it was written by a human. Now it's looking like the next iteration of the software, GPT-4, is just around the corner, with an estimated release date of sometime in early 2023. Despite the highly anticipated nature of this AI news, the exact details on GPT-4 have been pretty sketchy. OpenAI, the company behind GPT-4, has not publicly disclosed much information on the new model, such as its features or its abilities. Nevertheless, recent advances in the field of AI, particularly regarding Natural Language Processing (NLP), may offer some clues on what we can expect from GPT-4.

Terminologies like Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning are hype these days. Although these terms highly co-relate with each other, they also have distinctive features and specific use cases. AI deals with automated machines that solve problems and make decisions imitating human cognitive capabilities. Machine learning and deep learning are the subdomains of AI. Machine Learning is an AI that can make predictions with minimal human intervention.

Microsoft is discussing a potential $10 billion investment in ChatGPT founder OpenAI that will boost its value to $29 billion. The AI tool has dazzled many people worldwide due to its uncanny ability to generate text while mimicking human speech. According to Reuters, OpenAI told investors last month it expects $200 million in revenue in 2023 and a billion dollars by 2024. Most people know Microsoft as the creator of MS Word, PowerPoint, Excel, and Outlook. Meanwhile, OpenAI is an artificial intelligence laboratory made popular by ChatGPT.

Deep Learning and Reinforcement Learning are two of the most popular subsets of Artificial intelligence. The AI market was about $120 billion in 2022 and is increasing at a mind-boggling CAGR above 38%. As artificial intelligence evolved, these two approaches (RL and DL) have been used to solve many problems, including image recognition, machine translation, and decision-making for complex systems. We will explore how they work along with their applications, limitations, and differences in an easy-to-understand way. Deep Learning is the subset of machine learning in which we use Neural Networks to recognize patterns in the given data for predictive modeling on the unseen data.