Domo (Nasdaq: DOMO) today announced that it has achieved Amazon Web Services (AWS) Machine Learning Competency status in the new Applied Artificial Intelligence (Applied AI) category. This designation recognizes that Domo has demonstrated deep experience and expertise in building or integrating ML solutions on AWS. AWS Partners in the AWS Partner Network (APN) recognized as part of the AWS Machine Learning Competency expansion help customers take advantage of intelligent solutions for the business, from creating, automating, and managing end-to-end ML workflows to modernizing applications with machine intelligence. The AI and ML driven applications are maturing rapidly and creating new demands on enterprises. AWS is keeping pace and continuously evolving AWS Competency Programs to provide customers an ability to engage enhanced AWS Partner technology and consulting offerings.

Domo announced that it has achieved Amazon Web Services (AWS) Machine Learning Competency status in the new Applied Artificial Intelligence (Applied AI) category. This designation recognizes that Domo has demonstrated deep experience and expertise in building or integrating ML solutions on AWS. AWS Partners in the AWS Partner Network (APN) recognized as part of the AWS Machine Learning Competency expansion help customers take advantage of intelligent solutions for the business, from creating, automating, and managing end-to-end ML workflows to modernizing applications with machine intelligence. The AI and ML driven applications are maturing rapidly and creating new demands on enterprises. AWS is keeping pace and continuously evolving AWS Competency Programs to provide customers an ability to engage enhanced AWS Partner technology and consulting offerings.

Domo (Nasdaq: DOMO) today announced that it has achieved Amazon Web Services (AWS) Machine Learning Competency status in the new Applied Artificial Intelligence (Applied AI) category. This designation recognizes that Domo has demonstrated deep experience and expertise in building or integrating ML solutions on AWS. AWS Partners in the AWS Partner Network (APN) recognized as part of the AWS Machine Learning Competency expansion help customers take advantage of intelligent solutions for the business, from creating, automating, and managing end-to-end ML workflows to modernizing applications with machine intelligence. The AI and ML driven applications are maturing rapidly and creating new demands on enterprises. AWS is keeping pace and continuously evolving AWS Competency Programs to provide customers an ability to engage enhanced AWS Partner technology and consulting offerings.

Data is one of the most important assets a business has, and organizations around the world are increasingly taking advantage of the data they hold. However, how organizations understand data is changing, with an increasing focus on artificial intelligence and machine learning to help deliver new business insights from big data that humans cannot obtain themselves. In modern BI, there is value in data being interpreted by both humans and computer algorithms, and both are essential for organizations to utilize experience-led and data-driven insights. While BI allows you to answer what has happened, AI allows you to predict what will happen. Both are superpowers, and when coupled with a human's cognitive ability, a step function increase in productivity occurs.

Within the cloud space, AI is being considered for collaboration more and more as the likes of IBM, Amazon and Microsoft delve into this kind of technology. Automated management of hard drive-free data is bound to speed up the process of storage management. Also, AI, with its need for a large amount of processing power, can thrive within the cloud, which is known for its ability to manage large projects with ease. Cloud adoption is directing enterprises to AI, as vendors offer a growing number of tools and services, without big upfront investments. But according to Domo's VP of Data and Curiosity, Ben Schein, it's vital that the agility and speed that AI can provide is balanced with integration and control. To achieve this, Schein said it "comes down to a sense of empathy for the people that have to use intelligence".

Enterprise seems to be entering a new era ruled by data. What was once the realm of science fiction, AI in business intelligence is evolving into everyday business as we know it. Companies can now use machines algorithms to identify trends and insights in vast reams of data and make faster decisions that potentially position them to be competitive in real-time. It's not a simple process for companies to incorporate machine learning into their existing business intelligence systems, though Skymind CEO and past Emerj podcast guest Chris Nicholson advises that it doesn't have to be daunting. "AI is just a box," he says.

Today, it is no longer a question of adopting AI or not. Instead, ask yourself if you and your sales team are ready for the inevitable. Artificial intelligence for business is a reality. If your goal is to forge ahead and lead in your field, then you need to adapt to a workplace where AI plays a crucial role. As J.J. Kardwell, founder and CEO of predictive marketing software company EverString, puts it: "Growth-focused sales organizations of every size and stage cannot afford to ignore the benefits of AI-assisted sales."

As J.J. Kardwell, founder and CEO of predictive marketing software company EverString, puts it: "Growth-focused sales organizations of every size and stage cannot afford to ignore the benefits of AI-assisted sales." According to research done by Accenture, artificial intelligence is projected to improve labor productivity by as much as 40 percent. It is no surprise that investment in artificial intelligence has increased to $14.9 billion, as of 2014, according to a study conducted by the Bank of America Merrill Lynch. This is projected to increase further by at least 50 percent per year. The same study claims that the innate limitations of humans will make AI the core technology of the so-called "internet of things."

Treatable infectious diseases are a critical challenge for public health. Outreach campaigns can encourage undiagnosed patients to seek treatment but must be carefully targeted to make the most efficient use of limited resources. We present an algorithm to optimally allocate limited outreach resources among demographic groups in the population. The algorithm uses a novel multiagent model of disease spread which both captures the underlying population dynamics and is amenable to optimization. Our algorithm extends, with provable guarantees, to a stochastic setting where we have only a distribution over parameters such as the contact pattern between agents. We evaluate our algorithm on two instances where this distribution is inferred from real world data: tuberculosis in India and gonorrhea in the United States. Our algorithm produces a policy which is predicted to avert an average of least 8,000 person-years of tuberculosis and 20,000 person-years of gonorrhea annually compared to current policy.

Our map infographic shows the top tech startups in every US state, as determined by funding totals. Companies include Magic Leap, DraftKings, and Vox. The tech boom has diffused beyond the traditional hotbeds of California, New York, and Massachusetts, across the entire United States. In our latest map of the most well-funded American tech startup in each state, some companies with the deepest pockets were found in Florida (Magic Leap, $1.89B), Virginia (OneWeb, $2.2B), Utah (Domo, $698M), and Illinois (Avant, $655M). We'll look at 15 trends that we believe will shape the tech sector in 2018, ranging from the race for AI to car subscriptions and cross-border M&A.