By 2028, the global machine learning market will grow to $152.24 billion. As the digital economy continues its massive growth, events like the COVID-19 pandemic further accelerated consumers' demand for digital services. As a result, nearly all businesses across all sectors will seek to harness the power of artificial intelligence (AI) and machine learning. Before businesses can venture deep into the sci-fi movie-inspired possibilities of machine learning, it is important to be aware of its underlying principles and what makes it work. At the heart of every machine learning initiative, data labeling forms a key foundation of this disruptive technology.

Responsible Machine Learning development is essential to extract positive outcomes from various AI and Machine Learning initiatives. These initiatives empower AI engineers, data scientists and end-users to build, analyze and utilize various AI ML applications ethically. Almost every major technology innovation company evangelizes the importance of Responsible Machine Learning development is essential to extract positive outcomes from various AI and Machine Learning initiatives. One of them is Twitter. Twitter has constantly provided updates on its ongoing AI and Machine Learning projects.

Deep learning models (aka neural nets) now power everything from self-driving cars to video recommendations on a YouTube feed, having grown very popular over the last couple of years. Despite their popularity, the technology is known to have some drawbacks, such as the deep learning "reproducibility crisis"-- as it is very common for researchers at one to be unable to recreate a set of results published by another, even on the same data set. Additionally, the steep costs of deep learning would give any company pause, as the FAANG companies have spent over $30,000 to train just a single (very) deep net. Even the largest tech companies on the planet struggle with the scale, depth, and complexity of venturing into neural nets, while the same problems are even more pronounced for smaller data science organizations as neural nets can be both time-and cost-prohibitive. Also, there is no guarantee that neural nets will be able to outperform benchmark models like logistic regression or gradient-boosted ones, as neural nets are finicky and typically require added data and engineering complexities.

Machine learning (ML) is positioned to solve a range of supply chain challenges today, but commercializing ML applications lags behind research. Businesses that stand to gain from ML solutions are helping accelerate that process. Covid-19 has also spurred the effort as face-to-face commerce is discouraged. For example, chatbots are saving the auto insurance industry during the pandemic. Powered by machine learning, digital insurance platforms research applicants' driving records, analyze data, apply risk metrics to coverage and pricing, and issue policies remotely, according to Professor Daniela Rus, faculty director for CSAIL.

Amazon AMZN is making strong efforts toward capitalizing on the growing demand for Machine Learning (ML) based cloud services. This evident from its latest move where its cloud computing arm, Amazon Web Services (AWS) made Amazon Personalize available to general customers. Notably, it helps in training and deployment of private machine learning models. Notably, the new service is a fully managed one that aids in development of applications by managing the entire machine learning pipeline which includes algorithm selection, processing data, feature identification, and optimizing and hosting the results. During the application development, users will be able to address specific product recommendations, customized direct marketing and individualized search results.

Adoption of Machine Learning is growing significantly in business. More and more, the integration of Machine Learning is becoming an integral part of a digital transformation processes that businesses are looking to undergo. The advances in technology and the accessibility of Machine Learning capabilities, like for example TensorFlow or Cloud Services such as Google Cloud AI and operational tooling including Talend have helped combat the skills required to embrace Machine Learning concepts and accelerate delivery of solutions. Previously a discipline associated with scientists in white lab coats, Machine Learning is now becoming an increasingly mainstream activity. So instead of white lab coats you are more likely to find designer jeans and wearable devices associated with today's emerging army of machine learning developers.

There is no doubt that AI (artificial intelligence) is the new electricity and everyone is trying to get benefits from the trend. Many companies are integrating AI solutions in their business operations to reap the benefit of emerging machine learning (ML) technologies. The seamless introduction of AI, however, requires thoughtful adaptation of corporate strategy to requirements of this emerging technology. As a partner at a venture studio, I see companies try to get in the trenches of machine intelligence without the proper preparation. Here's what we recommend companies to advance their initiatives effectively.

There is no doubt that AI (artificial intelligence) is the new electricity and everyone is trying to get benefits from the trend. Many companies are integrating AI solutions in their business operations to reap the benefit of emerging machine learning (ML) technologies. The seamless introduction of AI, however, requires thoughtful adaptation of corporate strategy to requirements of this emerging technology. As a partner at a venture studio, I see companies try to get in the trenches of machine intelligence without the proper preparation. Here's what we recommend companies to advance their initiatives effectively.

I am certain you have heard of Artificial Intelligence. So, now that you have heard about it, you might be wondering what can Artificial Intelligence actually do for your company. Or is it just all hype? Well a lot of it is hype – I'm looking at you killer robots. As Andrew Ng said, "Fearing a rise of killer robots is like worrying about overpopulation on Mars".