Industrial automation can be defined as a Set of processes where implementation of control systems, such as Robots, computers or both, including information technologies for administering different processes and equipment's in an industry to replace a human being. This is done to achieve higher productivity, quality, flexibility, information accuracy and higher safety. The negative effects being high initial cost, associated with making the switch from a human involved production line to an automatic production line. In other words, it is the application of artificial intelligence and other advanced technologies like computer vision, cognitive automation, machine learning to a robotic process which will elevate competitive advantage to a business.
While many companies claim to provide "AI-driven" solutions, in reality they're leveraging machine learning techniques at best, developing what Ganzarski refers to as augmented intelligence. In an interview with Information Age, Ganzarski's discussed how he thinks this gap from augmented intelligence to Artificial Intelligence will be bridged, how long that will take and what the future of AI holds. Instead, tech companies claiming to do'AI' are actually providing what I would define as augmented intelligence – very sophisticated, fast decision processing or decision supporting software based on real-time scenarios. In the simplest of language, AI is a computer (software, robot, call it whatever you will) that has the ability to do things only a human can do, and use the same level of logic and reasoning that a human would.
Intel's research found that drivers are unclear about when a machine will talk and when it will listen; which gauges will be used and what they mean; and, perhaps most importantly, how much the driver has to pay attention in self-driving car mode. One of the major benefits, according to the Intel study, is that a computer making judgments about traffic and road conditions won't second-guess a decision. So how will drivers learn to trust autonomous cars? "Trust in autonomous vehicles will reach the first major milestone when the key car manufacturers complete testing on public roads under various environment conditions and will be able to share some positive results in terms of safe driving benchmarks," Ponomarev says.
With minimal pre-task human efforts needed, the scalability of unsupervised machine learning is much higher. David Dittman, director of business intelligence and analytics services at Procter & Gamble, explained that the biggest analytics problem he sees today with other large US companies is that "they are becoming enamored by [machine learning and analytics] technology, while not understanding that they have to build the foundation [for it], because it can be hard, expensive and requires vision." Supervised ML requires humans to create sets of training data and validate the results of the training. A similar shift is likely in machine learning, he suggested, as software and service providers begin to offer application programming interfaces to commercial machine learning platforms.
Machines would look at data, understand, reason over it, and they continue to learn: understand, reason and learn, not program, in my simple definition. There would be two big differences between business and consumer AI. It leads me to the second big difference between consumer and business AI. That gives you a long, long, long answer, but this is why I'm so positive this world will have more really tough problems solved with AI.
The number of times a "happy face" button is pressed at the exit compared to the number of "angry face" button pushes? The obvious solution of using a status indicator similar to traffic light (good/need to pay attention/problem) is often not viable as it requires an enormous amount of trust and courage: Trusting your team (and perhaps also the vendor's team) that they do the right thing – and having the courage to "let go" as a manager. The number of times a "happy face" button is pressed at the exit compared to the number of "angry face" button pushes? The obvious solution of using a status indicator similar to traffic light (good/need to pay attention/problem) is often not viable as it requires an enormous amount of trust and courage: Trusting your team (and perhaps also the vendor's team) that they do the right thing – and having the courage to "let go" as a manager.
The impacts are contributing by automating repetitive task, creating efficiencies, ubiquitously improving user experience, and creating ways for humans to improve our cognition. From a business perspective, enterprise executives are most optimistic about the potential of AI technologies to increase efficiencies via automated communications and alerts to enable more proactive approaches (70%) business challenges. Additionally, our surveyed execs believe virtual personal assistants and automated data analysts are the AI solutions they see most impacting their businesses. Business execs also see potential for AI managers to improve life for employees.
Waymo--the Google self-driving project that spun out to become a business under Alphabet--said Monday it's using Intel chips as part of a compute platform that allows its self-driving Chrysler Pacifica hybrid minivans to process huge amounts of data so it can make decisions in real time while navigating city streets. "As the most advanced vehicles on the road today, our self-driving cars require the highest-performance computers to make safe driving decisions in real time," Waymo CEO John Krafcik said in an emailed statement. However, it wasn't until Waymo started the Chrysler Pacifica minivan project that it began working more closely with the chipmaker. "By working closely with Waymo, Intel can offer Waymo's fleet of vehicles the advanced processing power required for level 4 and 5 autonomy."
For months now, major companies have been hooking up--Uber and Daimler, Lyft and General Motors, Microsoft and Volvo--but Intel CEO Brian Krzanich's announcement on Monday that the giant chipmaker is helping Waymo, Google's self-driving car project, build robocar technology registers as some seriously juicy gossip. Krzanich said Monday that Waymo's newest self-driving Chrysler Pacificas, delivered last December, use Intel technology to process what's going on around them and make safe decisions in real time. And last year, Google announced it had created its own specialized chip that could help AVs recognize common driving situations and react efficiently and safely. "Our self-driving cars require the highest-performance compute to make safe driving decisions in real-time," Waymo CEO John Krafcik said in a statement.
Your organization is looking for ways to engage more closely with customers, improve decision making, improve the operational efficiency of manufacturing, or deliver better patient healthcare outcomes. We define "dark data" as large, often unstructured, data sets collected and stored from both internal and external sources that is not currently being used to create insights that deliver business value. Companies are struggling to adapt their data management architectures to handle the volume and velocity. You need the power of Artificial Intelligence and machine learning tools, to automate the understanding, management, and the extraction of valuable information from dark data.