On the second day of Amazon Web Services (AWS) re:Invent, Swami Sivasubramanian, vice president of AWS Data and Machine Learning (ML) revealed the latest innovations during his keynote. To start, Sivasubramanian announced the launch of Amazon Athena for Apache Spark, which he said will provide organizations with a more intuitive way to run complex data analytics. He noted that Apache Spark will run three times faster on AWS. The next product announcement was of the general availability of Amazon DocumentDB Elastic Clusters, a fully-managed solution to quickly scale document workloads of any size. Amazon SageMaker now supports Geospatial ML, giving access to multiple new kinds of data.

The U.S. and almost all countries today identify AI as a critical strategic area in the future of computing. Companies are more invested than ever in discovering how AI can provide advantages in their competitive markets. According to a report released earlier this year by Appen Limited, AI budgets increased 55% year over year, ranging from $500,000 to $5 million, with more attention placed on internal processes, a better understanding of data and efficiency gains. Fueling this interest are super-accelerated digital transformations driven by a "digital or die" theme mitigating the limitations imposed by the Covid-19 pandemic. With digitization, the volume, variety and velocity of data have increased exponentially for many years. Capturing, managing and exploiting the data proved challenging.

Machine learning (ML) is more than a technology. It's a sustainable solution that can help organizations build eco-friendly business models. In recent years, machine learning has also become more accessible, enabling leaders to take advantage of technology that can bolster business --and promote a better future for the planet. The commercial real estate sector has made some progress in making buildings more sustainable. With machine learning, organizations can monitor and report environmental impact on demand, efficiently manage data streams through AI, and reduce carbon emissions by closing the gap between the digital and the physical divide in the workplace. Carbon Lighthouse is an example of a company that is using machine learning to achieve sustainability in the commercial real estate sector using advanced solutions to make decarbonization easy and profitable.

Bushfires in Australia are as commonplace as kangaroos and koalas. A hot, dry climate regularly sets the stage for conflagrations that endanger human lives, property, and wildlife and threaten one of the country's top economic industries: wine. Fires during summer 2019–2020 decimated entire vineyards in South Australia, Victoria and New South Wales, but smoke, which was far more widespread and insidious, seeped into grapes and into fermenting barrels, yielding unpleasant, unsaleable product. Although the full extent of the damage caused has not yet been calculated, analysis from the Australian Wine Research Institute indicates that smoke taint alone costs the country's wine industry tens to hundreds of millions of dollars each time a high fire season occurs. Advances in a wide range of technologies could help growers and winemakers mitigate the negative impact of smoke taint and other unpredictable anomalies, such as frost, drought, pests, and disease -- and not just in Australia, but around the world.

The training also looks at artificial intelligence, machine learning, and image processing.

"Our acquisition of Dynamic Yield has brought us a lot of excitement," says McDonald's CEO Steve Easterbrook. "Very simply put, in the online world when we're shopping and we pick an item and put it into our shopping basket, any website will automatically suggest two or three things to go along with it. We're the first business that we're aware of that can bring that into the physical world. Steve Easterbrook, CEO of McDonald's, discusses how the company is using technology to elevate the customer experience and accelerate growth in an interview on CNBC: As we've executed the growth plan we've spent the first two years, three or four years ago, turning the business around. Now we've had a couple of years of growth.

Like most sectors, the solar industry is rapidly embracing ways to analyze and crunch data in order to lower the cost of solar energy and to open up new markets for their technology. The rise of data tools--algorithms, machine learning, sensors--are driving investments in, and acquisitions of, solar startups, while entrepreneurs are launching new companies that are using data to solve various solar industry problems. Meanwhile, big companies are spending money on tracking, monitoring and evaluating data from solar projects worldwide, helping to lower the cost of generating energy from the sun. It shouldn't come as a surprise that the solar sector is the latest to embrace the value of data. Other traditionally non-digital sectors, like the auto industry, oil and gas, and agriculture are turning to managing data as a necessity to keep their technology competitive and their companies in business.

It is almost impossible to escape the impact frontier technologies are having on everyday life. At the core of this impact are the advancements of artificial intelligence, machine learning, and deep learning. These change agents are ushering in a revolution that will fundamentally alter the way we live, work, and communicate akin to the industrial revolution more specifically, AI is the new industrial revolution. The most exciting and promising of these frontier technologies is the advancements happening in the design. Design has consistently played a role in shaping the value of technological evolution to fit our lives, not merely as consumers or gadget lovers, but as people - respecting our real needs and behaviors.

Like most sectors, the solar industry is rapidly embracing ways to analyze and crunch data in order to lower the cost of solar energy and to open up new markets for their technology. The rise of data tools--algorithms, machine learning, sensors--are driving investments in, and acquisitions of, solar startups, while entrepreneurs are launching new companies that are using data to solve various solar industry problems. Meanwhile, big companies are spending money on tracking, monitoring and evaluating data from solar projects worldwide, helping to lower the cost of generating energy from the sun. It shouldn't come as a surprise that the solar sector is the latest to embrace the value of data. Other traditionally non-digital sectors, like the auto industry, oil and gas, and agriculture are turning to managing data as a necessity to keep their technology competitive and their companies in business.

Like most sectors, the solar industry is rapidly embracing ways to analyze and crunch data in order to lower the cost of solar energy and to open up new markets for their technology. The rise of data tools--algorithms, machine learning, sensors--are driving investments in, and acquisitions of, solar startups, while entrepreneurs are launching new companies that are using data to solve various solar industry problems. Meanwhile, big companies are spending money on tracking, monitoring and evaluating data from solar projects worldwide, helping to lower the cost of generating energy from the sun. It shouldn't come as a surprise that the solar sector is the latest to embrace the value of data. Other traditionally non-digital sectors, like the auto industry, oil and gas, and agriculture are turning to managing data as a necessity to keep their technology competitive and their companies in business.