Alphabet's Sidewalk Labs urban tech division today detailed a commercial building kit dubbed Mesa that uses real-time data and automation to optimize energy usage. Sidewalk claims it's able to cut waste and cost while simplifying installation and management. Commercial buildings have an outsized environmental impact, Sidewalk Labs notes, making up for nearly 30% of greenhouse emissions from buildings in New York City alone. In response, at least 31 U.S. metros have passed laws establishing power benchmarks or reporting mandates, with 15 requiring energy performance targets. But older buildings often lack the requisite technologies found in more modern construction.
First responders have been busier than usual in 2020, but it's not just the seemingly escalating quantity of emergencies that's been difficult. In the grips of a pandemic, and with natural calamities ranging from fires in the west to devastating hurricanes in the east, the complexity of emergencies is growing, as has had the likelihood of fatal mistakes. For first responders and dispatchers, this often means allocating resources and initiating relief actions without a full picture of the consequences or a full understanding of the risks involved. Artificial Intelligence, advanced statistics, and machine learning can help untangle the dynamic variables of complex emergencies, and increasingly emergency dispatch and coordination is relying on these technologies to unify municipal and state response services. A company called Hexagon, which is now working with several government agencies around the world, recently unveiled what it calls Smart Advisor, a system that mines operational data in real-time to fill blind spots and alert agencies to the potential onset of complex emergencies.
Iguazio, the data science platform built for production and real-time machine learning applications, announced it has been deployed by mobile software company PadSquad, to improve the relevance and performance of the digital campaigns they run for their customers worldwide. PadSquad is revolutionizing traditional media with interactive features and innovative technologies that transform the audiences' experience and engagement with ad creatives. Iguazio was deployed by PadSquad to use AI to improve ad performance and reduce media costs for their customers. They do this by ingesting and acting upon real-time events – from contextual content on the page, engagement with creative elements like video views, swipeable panels, and hot spots, to the season and time of day – at a rate of over 3,000 events per second. Utilizing online and offline behavioral data from multiple sources, available to them through third-party platforms and their own internal tools, Padsquad can now harness machine learning to optimize ad performance and provide a better and more personalized user experience for their customers' audiences.
AI pilots are progressing into production based on their combined contributions to improving customer experience, stabilizing and increasing revenues, and reducing costs. The most successful AI use cases contribute to all three areas and deliver measurable results. Of the many use cases where AI is delivering proven value in enterprises today, the ten areas discussed below are notable for the measurable results they are providing. What each of these ten use cases has in common is the accuracy and efficiency they can analyze and recommend actions based on real-time monitoring of customer interactions, production, and service processes. Enterprises who get AI right the first time build the underlying data structures and frameworks to support the advanced analytics, machine learning, and AI techniques that show the best potential to deliver value.
For most of us, the building years of our lives were shaped by the books we read. A generation acquired its knowledge caressing through dry books with stenciled alphabets. From learning our ABCs to Shakespeare's sonnets, Industrial Revolution ensured that its role in shaping world history through its machinery, chemicals, steam and more, is kept alive and documented via its own production of printing machines. The Industrial Revolution paved the way for the life we know today and far surpassed the era of simplistic conveyor belts and heavy manual surveillance. Production lines employ machinery and humans alike.
The healthcare industry has come a long way since its inception a few years back. Long gone are those days when each process in healthcare was running manually. Today, the situation is different. All thanks to the advancement in technology and the wave of digitization, healthcare is experiencing a paradigm shift in its processes like never before. It is a matter of immense pride that today we have the robots performing surgeries, taking care of elderly people, and most of all, helping doctors in more precise decision making.
Artificial intelligence (AI) and the Internet of Things (IoT) are two incredibly popular acronyms that have newly emerged as a disruptive force within the technological world. In recent years, AI and IoT have revolutionized and transformed how the world interacts. Now, this advancement has progressed even further with a new innovative and exciting combination called Artificial Intelligence of Things (AIoT). Let's provide a bit of context. The definition of AI is intelligence-driven machines that perform human-like tasks, such as a virtual assistant, self-awareness and automation activities.
Two-thirds of senior executives across industries -- and nearly nine out of ten leaders from the world's largest enterprises -- believe AI is vitally important for the future of their businesses and will be increasing AI investment in the post-pandemic era. However, significant challenges remain on delivering ROI from AI investment. An ESI ThoughtLab study of 1,200 organisations has revealed that companies are generating on average an ROI of only 1.3%, while 40% of AI projects are not yet profitable. The reason for this, according to the research, is that AI initiatives require time, expertise and scale to deliver on their promise of high returns. With the pandemic speeding up the need for quick data-driven decision-making, companies should act now to develop the skills, platforms, and processes that can enable them to achieve the full strategic, operational, and financial benefits from AI.
By Swapnil Shinde, a three-time entrepreneur, angel investor, and CEO and co-founder of Zeni, the automated finance management platform for startups. If you've found yourself thinking, "There's an AI-powered solution for everything these days," you're not far off. Today, more than 37% of organizations have implemented artificial intelligence (AI) in some form, and it is estimated that by 2021, 80% of emerging technologies will have AI foundations. When successfully executed, AI is a powerful tool for businesses and consumers alike. It can help businesses scale faster by automating time-consuming processes, create deeper connections with customers through hyper-personalized interactions, make smarter business decisions with access to real-time data from across an organization, plus so much more.
If you've found yourself thinking, "There's an AI-powered solution for everything these days," you're not far off. Today, more than 37% of organizations have implemented artificial intelligence (AI) in some form, and it is estimated that by 2021, 80% of emerging technologies will have AI foundations. When successfully executed, AI is a powerful tool for businesses and consumers alike. It can help businesses scale faster by automating time-consuming processes, create deeper connections with customers through hyper-personalized interactions, make smarter business decisions with access to real-time data from across an organization, plus so much more. But time and time again, we read about promising AI-powered startups coming up short, shutting down their businesses because they've failed to build meaningful AI solutions for the problems they hoped to solve. After 10-plus years of leveraging AI, machine learning and natural language processing to build and grow successful AI-powered platforms, I've identified three key areas where most startups and businesses go wrong when building AI-powered platforms.