Mediaboss Marketing

Register for this live video webcast - Friday, March 27 at 10 AM PT Ask the expert - get your AI/RPA questions answered by an industry expert.

One of the keys to digital transformation that most fashionable term is creating a management structure in which everything is accountable to data analytics. In a related trend, robotic process automation (RPA) helps automate a company's work flow and business processes. At its most optimum, RPA is driven by an AI-based analytics platform.

These key emerging technologies are the focus on this webinar. By attending, you will learn:

To provide insight into digital transformation, RPA and AI, I'll speak with Amir Orad, CEO, Sisense

Titl

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Amir Orad, CEO, Sisense

James Maguire, Managing Editor, Datamation moderator

Bring your questions to this live video webcast well answer as many as we can.

Register for this live video webcast - Friday, March 27 at 10 AM PT

Robotic Process Automation: Pros and Cons

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How Artificial Intelligence is Changing Healthcare

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Artificial Intelligence Trends: Expert Insight on AI and ML Trends

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12 Examples of Artificial Intelligence: AI Powers Business

FEATURE|ByJames Maguire, September 13, 2019

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Artificial Intelligence and RPA: Keys to Digital Transformation - Datamation

Verified Market Research recently published a research report titled, Artificial Intelligence as a Service Market Study Report 2020. The research report is created based on historical and forecast data derived from researchers using primary and secondary methods. The Artificial Intelligence as a Service market is one of the fastest-growing markets and is expected to witness substantial growth in the forecast years. Reader are provided easy access to thorough analysis on the various aspects such as opportunities and restraints affecting the market. The report clearly explains the trajectory this market will take in the forecast years.

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Tags: Artificial Intelligence as a Service Market Size, Artificial Intelligence as a Service Market Trends, Artificial Intelligence as a Service Market Forecast, Artificial Intelligence as a Service Market Growth, Artificial Intelligence as a Service Market Analysis

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Artificial Intelligence as a Service Market Growth by Top Companies, Trends by Types and Application, Forecast to 2026 - Bandera County Courier

Artificial intelligence is all the rage these days. In the popular media, regular cyber systems seem almost passe, as writers focus on AI and conjure up images of everything from real-life Terminator robots to more benign companions. In intelligence circles, Chinas uses of closed-circuit television, facial recognition technology, and other monitoring systems suggest the arrival of Big Brotherif not quite in 1984, then only about forty years later. At the Pentagon, legions of officers and analysts talk about the AI race with China, often with foreboding admonitions that the United States cannot afford to be second in class in this emerging realm of technology. In policy circles, people wonder about the ethics of AIsuch as whether we can really delegate to robots the ability to use lethal force against Americas enemies, however bad they may be. A new report by the Defense Innovation Board lays out broad principles for the future ethics of AI, but only in general terms that leave lots of further work to still be done.

What does it all really mean and is AI likely to be all its cracked up to be? We think the answer is complex and that a modest dose of cold water should be thrown on the subject. In fact, many of the AI systems being envisioned today will take decades to develop. Moreover, AI is often being confused with things it is not. Precision about the concept will be essential if we are to have intelligent discussions about how to research, develop, and regulate AI in the years ahead.

AI systems are basically computers that can learn how to do things through a process of trial and error with some mechanism for telling them when they are right and when they are wrongsuch as picking out missiles in photographs, or people in crowds, as with the Pentagon's "Project Maven"and then applying what they have learned to diagnose future data. In other words, with AI, the software is built by the machine itself, in effect. The broad computational approach for a given problem is determined in advance by real old-fashioned humans, but the actual algorithm is created through a process of trial and error by the computer as it ingests and processes huge amounts of data. The thought process of the machine is really not that sophisticated. It is developing artificial instincts more than intelligenceexamining huge amounts of raw data and figuring out how to recognize a cat in a photo or a missile launcher on a crowded highway rather than engaging in deep thought (at least for the foreseeable future).

This definition allows us quickly to identify some types of computer systems that are not, in fact, AI. They may be important, impressive, and crucial to the warfighter but they are not artificial intelligence because they do not create their own algorithms out of data and multiple iterations. There is no machine learning involved, to put it differently. As our colleague, Tom Stefanick, points out, there is a fundamental difference between advanced algorithms, which have been around for decades (though they are constantly improving, as computers get faster), and artificial intelligence. There is also a difference between an autonomous weapons system and AI-directed robotics.

For example, the computers that guide a cruise missile or a drone are not displaying AI. They follow an elaborate, but predetermined, script, using sensors to take in data and then putting it into computers, which then use software (developed by humans, in advance) to determine the right next move and the right place to detonate any weapons. This is autonomy. It is not AI.

Or, to use an example closer to home for most people, when your smartphone uses an app like Google Maps or Waze to recommend the fastest route between two points, this is not necessarily, AI either. There are only so many possible routes between two places. Yes, there may be dozens or hundredsbut the number is finite. As such, the computer in your phone can essentially look at each reasonable possibility separately, taking in data from the broader network that many other peoples phones contribute to factor traffic conditions into the computation. But the way the math is actually done is straightforward and predetermined.

Why is this important? For one thing, it should make us less breathless about AI, and see it as one element in a broader computer revolution that began in the second half of the twentieth century and picked up steam in this century. Also, it should help us see what may or may not be realistic and desirable to regulate in the realm of future warfare.

The former vice chairman of the joint chiefs of staff, Gen. Paul Selva, has recently argued that the United States could be about a decade away from having the capacity to build an autonomous robot that could decide when to shoot and whom to killthough he also asserted that the United States had no plans actually to build such a creature. But if you think about it differently, in some ways weve already had autonomous killing machines for a generation. That cruise missile we discussed above has been deployed since the 1970s. It has instructions to fly a given route and then detonate its warhead without any human in the loop. And by the 1990s, we knew how to build things like skeet submunitions that could loiter over a battlefield and look for warm objects like tanksusing software to decide when to then destroy them. So the killer machine was in effect already deciding for itself.

Even if General Selva's terminator is not built, robotics will in some cases likely be given greater decisionmaking authority to decide when to use force, since we have in effect already crossed over this threshold. This highly fraught subject requires careful ethical and legal oversight, to be sure, and the associated risks are serious. Yet the speed at which military operations must occur will create incentives not to have a person in the decisionmaking loop in many tactical settings. Whatever the United States may prefer, restrictions on automated uses of violent force would also appear relatively difficult to negotiate (even if desirable), given likely opposition from Russia and perhaps from other nations, as well as huge problems with verification.

For example, small robots that can operate as swarms on land, in the air or in the water may be given certain leeway to decide when to operate their lethal capabilities. By communicating with each other, and processing information about the enemy in real-time, they could concentrate attacks where defenses are weakest in a form of combat that John Allen and Amir Husain call hyperwar because of its speed and intensity. Other types of swarms could attack parked aircraft; even small explosives, precisely detonated, could disable wings or engines or produce secondary and much larger explosions. Many countries will have the capacity to do such things in the coming twenty years. Even if the United States tries to avoid using such swarms for lethal and offensive purposes, it may elect to employ them as defensive shields (perhaps against North Korean artillery attack against Seoul) or as jamming aids to accompany penetrating aircraft. With UAVs that can fly ten hours and one hundred kilometers now costing only in the hundreds of thousands of dollars, and quadcopters with ranges of a kilometer more or less costing in the hundreds of dollars, the trendlines are clearand the affordability of using many drones in an organized way is evident.

Where regulation may be possible, and ethically compelling, is limiting the geographic and temporal space where weapons driven by AI or other complex algorithms can use lethal force. For example, the swarms noted above might only be enabled near a ship, or in the skies near the DMZ in Korea, or within a small distance of a military airfield. It may also be smart to ban letting machines decide when to kill people. It might be tempting to use facial recognition technology on future robots to have them hunt the next bin Laden, Baghdadi, or Soleimani in a huge Mideastern city. But the potential for mistakes, for hacking, and for many other malfunctions may be too great to allow this kind of thing. It probably also makes sense to ban the use of AI to attack the nuclear command and control infrastructure of a major nuclear power. Such attempts could give rise to use them or lose them fears in a future crisis and thereby increase the risks of nuclear war.

We are in the early days of AI. We cant yet begin to foresee where its going and what it may make possible in ten or twenty or thirty years. But we can work harder to understand what it actually isand also think hard about how to put ethical boundaries on its future development and use. The future of warfare, for better or for worse, is literally at stake.

Retired Air Force Gen. Lori Robinson is a nonresident senior fellow on the Security and Strategy team in the Foreign Policy program at Brookings. She was commander of all air forces in the Pacific.

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The Evolution of Artificial Intelligence and Future of National Security - The National Interest

The book Design, Launch, and Scale IoT Servicesclassifies the components of IoT services into technical modules. One of the most important of these is Artificial Intelligence (AI). This article is intended to supplement the book by providing insight into AIand its applications for IoT.

After many years in the wilderness, AIis back on the hype curve and will change the world again. Or, will it? AIhas always been interesting, but what has changed to justify the current hype?

There are several contributing factors. The volumes of data that will be produced by many IoT services suggest that this data cannot be managed by humans with traditional analytics tools. Therefore, AIcan offer opportunities for IoT services to extract maximum value from the data. IoT cloud platforms are now offering AIservices via APIs and application development tools, making AI more accessible for many IoT services. Now, AIcan be incorporated without requiring extensive development or excessive costs.

AI can perform the Treble A actions automatically but there is a cost associated with every step in the lifecycle, therefore business owners should ask themselves why they should introduce AI. Understanding the end-goal is the starting point. Its not suitable for all services and requires evaluation to understand when and how it should be introduced.

The following questions can provide a useful starting point for evaluating the introduction of AI:

The majority of IoT services include (or claim to include) some aspect of AIin their solution. This is due to a wide diversity in AI definitions (supervised/unsupervised, reinforced/deep learning) and the hype surrounding AI. (Note: All IoT services should take advantage of this hype while it lasts.)

Lets look at the most common AI features and IoT industries to consider how IoT service owners can best evaluate AI and answer the questions above.

IoT cloud platform providers are offering powerful AIvisual recognition APIs. For example, developing a human visual recognition tool has now become a trivial exercise for developers, and the cost of using visual recognition in IoT services has reduced drastically. These tools are best used for use cases recognizing humans and objects, but may not be useful for very precise recognition use cases. Developing specific visual recognition capabilities proves too expensive for most services, but it does make the service more attractive for end-users.

Robotics is a branch of AIthat, for many, implies a 2-armed, 2-legged machine that communicateswith humans using visual or voice recognition. However, the most important use cases for IoT robotics involve the collection of data from sensors or extracted from robot programs. This data can be used by IoT services as input for AImachine learning algorithms to increase robot efficiency, implementing features such as predictive fault management or adaptive positioning. AIcan be used to increase productivity with robotic systems as part of Industrial IoT services that will become vital for many Industry 4.0 use cases.

Natural Language Processing (NLP) and voice recognition features have become widely available in mobile phones and CRM (customer relationship management) systems. They can be implemented via IoT cloud service APIs. This will be an option for many IoT services without requiring significant investment. It will make most services more attractive, implyingmore sales.However, we are probably quite far off from the stage where NLP is fundamental for IoT services. Its available on many mobile apps, but most users still prefer to use a touch screen. The main use cases for voice control systems will most likely involve voice to text transcription for operational or CRM activities to reduce cost but may increase frustration for end-users.(Note: Cloud providers are also introducing AIaudio recognition APIs for fault detection that can be used to replace or augment visual recognition features.)

Smart factories offer numerous opportunities for implementing use cases that can increase efficiency via visual inspection, checking for faulty components or assembly processes errors. The analysis required should include cost vs benefits. If visual inspection slows the production process, it may be counterproductive to introduce in a manufacturing processthat has a low fault rate.

For example, lets say that a smart factory is creating 5,000components per day averaging 50 faulty components. The introduction of a visual inspection may reduce the components to 0 faults. However, if it slows the manufacturing process to produce only 4,000components per day, is it worthwhile? The process owner will have to calculate if the reduction in throughput outweighs the benefits of a reduction in faulty components. This is an example of real-time fault detection that can used for industrial IoT services. (Note: Many of the IoT Cloud platform providers offer the possibility to implement AIon edge devices, thus increasing the number use cases for real-time AI.)

Many industrial IoT solutions suggest that visual recognition will be used to determine thecurrent health and emotional status of machine operators. This would require quite advanced featuresto be beneficial, and therefore,its unlikely to be relevant for most IoT services.

Visual inspection shows great promise in detecting cancer and other ailments using advanced AItechniques and is improving the accuracy of diagnosis in many IoT health use cases. Very often, visual inspection requires large volumes of sample cases and training sets to ensure that the performance is acceptable. Genome technology generates billions of data items mapping our DNA that cannot be handled by humans and analytics tools. The introduction of AIoffers the possibility to predict future health issues. Using data volumes of this magnitude requires unsupervised learning techniques, such as clustering. This may prove too complex and expensive for the majority of current IoT use cases. Again, cloud service providers provide options facilitating the management of training models and data with tools such as Google Cloud AutoML. However, its likely this will only be cost-effective for a limited number of IoT services.

Its surprising that we havent yet seen the widespread deployment of AIin the management of intelligent hospitals. As with any complex logistical processes, AIcan create significant efficiencies with relatively low investment.

Many smart home IoT services will implement voice recognition that connectswith smart speakers. These are widely available from providers such as Amazon, Google and Apple.They can communicate with most smart home devices without significant complexity. Its likely that voice recognition will be an add-on for the majority of IoT services; nice to have, but not fundamental. Therefore, in most cases, IoT business owners may have to budget for this as a premium service.

The potential of AI in transportation is very exciting (i.e. driverless cars.) There will be a lot of innovation with AIfor drivers, but new IoT service owners will have to carve out a niche in this market. Although the technology is available, we may still be quite a way off from many use cases being acceptable for drivers. Imagine all the cars on the road communicating with each other and learning from one another as they hit the road.

One example to consider: Car A detects ice on the road,informs other cars and they all proceed to automatically adjust speed and brakes based on performance data from the other cars. This may seem futuristic, but the technology is currently available and AI offers the possibility of increased performance and decision making.

Analytics is closely interlinked with AI. When utilizing AI, its typical to ask yourself if you need analytics tools or if analytics will die due to the implementation of AI. The answer? Not quite.Most IoT services employ analytics, and therefore the data required by AI will already be available. AIshould be able to replace a lot of the activities performed by humans using analytics tools. Or, the output of analytics can be the starting point of AIsintroduction in many IoT services. The latter doesnt imply analytics are a prerequisite. If the data is available, expert systems can be developed without analytics.

Now, were starting to see augmented analytics. This is where AI assists analytics with intelligent searching and other tasks. This may not be necessary for most IoT services, but we can be sure that its being used by the massive tech companies around the world. Unfortunately, most IoT services wont generate enough data to be cost-effective to introduce.

Analytics, statistics and lies are often interchangeable. These wont be solved by AI. One challenge for many IoT services is that neural networks and deep learning AI techniques cannot explain why theyre making decisions. This can reduce customer confidence and will be unsuitable for IoT services where a clear understanding of a decision-making process is important.

Original post:
Artificial Intelligence Applications: Is Your Business Implementing AI Smartly? - IoT For All

Artificial intelligence has reached the inflection point where its less of a trend than a core ingredient across virtually every aspect of computing. These companies are applying the technology to everything from treating strokes to detecting water leaks to understanding fast-food orders. And some of them are designing the AI-ready chips that will unleash even more algorithmic innovations in the years to come.

For enabling the next generation of AI applications with its Intelligent Processing Unit AI chip

As just about every aspect of computing is being transformed by machine learning and other forms of AI, companies can throw intense algorithms at existing CPUs and GPUs. Or they can embrace Graphcores Intelligence Processing Unit, a next-generation processor designed for AI from the ground up. Capable of reducing the necessary number crunching for tasks such as algorithmic trading from hours to minutes, the Bristol, England, startups IPUs are now shipping in Dell servers and as an on-demand Microsoft Azure cloud service.

Read more about why Graphcore is one of the Most Innovative Companies of 2020.

For tutoring clients like Chase to fluency in marketing-speak

Ever tempted to click on the exciting discount offered to you in a marketing email? That might be the work of Persado, which uses AI and data science to generate marketing language that might work best on you. The companys algorithms learn what a brand hopes to convey to potential customers and suggests the most effective approachand it works. In 2019, Persado signed contracts with large corporations like JPMorgan Chase, which signed a five-year deal to use the companys AI across all its marketing. In the last three years, Persado claims that it has doubled its annual recurring revenue.

For becoming a maven in discerning customer intent via messaging apps

We may be a long way from AI being able to replace a friendly and knowledgeable customer-service representative. But LivePersons Conversational AI is helping companies get more out of their human reps. The machine-learning-infused service routes incoming queries to the best agent, learning as it goes so that it grows more accurate over time. It works over everything from text messaging to WhatsApp to Alexa. With Conversational AI and LivePersons chat-based support, the companys clients have seen a two-times increase in agent efficiency and a 20% boost in sales conversions compared to voice interactions.

For catalyzing care after a patients stroke

When a stroke victim arrives at the ER, it can sometimes be hours before they receive treatment. Viz.ai makes an artificial intelligence program that analyzes the patients CT scan, then organizes all the clinicians and facilities needed to provide treatment. This sets up workflows that happen simultaneously, instead of one at a time, which collapses how long it takes for someone to receive treatment and improves outcomes. Viz.ai says that its hospital customer base grew more than 1,600% in 2019.

For transforming sketches into finished images with its GauGAN technology

GauGAN, named after post-Impressionist painter Paul Gauguin, is a deep-learning model that acts like an AI paintbrush, rapidly converting text descriptions, doodles, or basic sketches into photorealistic, professional-quality images. Nvidia says art directors and concept artists from top film studios and video-game companies are already using GauGAN to prototype ideas and make rapid changes to digital scenery. Computer scientists might also use the tool to create virtual worlds used to train self-driving cars, the company says. The demo video has more than 1.6 million views on YouTube.

For bringing savvy to measuring the value of TV advertising and sponsorship

Conventional wisdom has it that precise targeting and measuring of advertising is the province of digital platforms, not older forms of media. But Hives AI brings digital-like precision to linear TV. Its algorithms ingest video and identify its subject matter, allowing marketers to associate their ads with relevant contentsuch as running a car commercial after a chase scene. Hives Mensio platform, offered in partnership with Bain, melds the companys AI-generated metadata with info from 20 million households to give advertisers new insights into the audiences their messages target.

For moving processing power to the smallest devices, with its low-power chips that handle voice interactions

Semiconductor company Syntiant builds low-power processors designed to run artificial intelligence algorithms. Because the companys chips are so small, theyre ideal for bringing more sophisticated algorithms to consumer tech devicesparticularly when it comes to voice assistants. Two of Syntiants processors can now be used with Amazons Alexa Voice Service, which enables developers to more easily add the popular voice assistant to their own hardware devices without needing to access the cloud. In 2019, Syntiant raised $30 million from the likes of Amazon, Microsoft, Motorola, and Intel Capital.

For plugging leaks that waste water

Wint builds software that can help stop water leaks. That might not sound like a big problem, but in commercial buildings, Wint says that more than 25% of water is wasted, often due to undiscovered leaks. Thats why the company launched a machine-learning-based tool that can identify leaks and waste by looking for water use anomalies. Then, managers for construction sites and commercial facilities are able to shut off the water before pipes burst. In 2019, the companys attention to water leaks helped it grow its revenue by 400%, and it has attracted attention from Fortune 100 companies, one of which reports that Wint has reduced its water consumption by 24%.

For serving restaurants an intelligent order taker across app, phone, and drive-through

If youve ever ordered food at a drive-through restaurant and discovered that the items you got werent the ones you asked for, you know that the whole affair is prone to human error. Launched in 2019, Interactions Guest Experience Platform (GXP) uses AI to accurately field such orders, along with ones made via phone and text. The technology is designed to unflinchingly handle complex custom ordersand yes, it can ask you if you want fries with that. Interactions has already handled 3 million orders for clients youve almost certainly ordered lunch from recently.

For giving birth to Kai (born from the same Stanford research as Siri), who has become a finance whiz

Kasisto makes digital assistants that know a lot about personal finance and know how to talk to human beings. Its technology, called KAI, is the AI brains behind virtual assistants offered by banks and other financial institutions to help their customers get their business done and make better decisions. Kasisto incubated at the Stanford Research Institute, and KAI branched from the same code base and research that birthed Apples Siri assistant. Kasisto says nearly 18 million banking customers now have access to KAI through mobile, web, or voice channels.

Read more about Fast Companys Most Innovative Companies:

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The 10 most innovative artificial intelligence companies of 2020 - Fast Company