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All of us have heard of the term artificial intelligence. Often abbreviated simple as AI, this technology has actually existed since before the 1970s. The only major difference is that it was nearly impossible to implement on a large-scale basis until relatively recently. AI is used as a predictive tool within online search engines.

It determines what pop-up advertisements you see when visiting specific websites. It can even be used to enhance the security and comfort of your home. So, it only makes sense that artificial intelligence has made its presence known within the world of virtual sports betting. Lets take a look at why this is great news for players and the entire industry.

One notable advantage of artificial intelligence involves the ability to create more accurate odds in relation to a specific sporting event. This is accomplished through the use of advanced algorithms and all technicalities aside, players are simple provided with more information during any given session. This enables them to make relevant decisions at the most appropriate times; increasing their chances of walking away a winner. Platforms with a Greater Degree of PersonalisationArtificial intelligence can also be used to create a more personalized betting experience. Here are some examples to consider:

It is therefore clear to understand why the majority of sports betting enthusiasts are keen to become involved with a provider that is able to offer a more organic experience.

We need to keep in mind that the presence of artificial intelligence can be seen across the entire online gaming industry. Whether referring to entertaining platforms such as Mega Moolah which offer truly massive jackpots or slots with player-specific bonuses, the future is here today.However, there is one major difference in terms of sports betting. AI has the ability to collate massive amounts of data at any given time. Those who are provided with more information are more likely to make informed wagering decisions. In the past, this would have to be performed manually. Artificial intelligence programs can now scour the Internet for the latest sports-related news and updates in a matter of seconds.

We should finally end by addressing an important question. Is artificial intelligence set to dominate the world of online sports betting into the foreseeable future? Some industry analysts firmly believe this observation while others claim that the human element will remain at the center. Either way, it will be interesting to see what is in store.Above all, even the most advanced AI platforms can only go so far. Successful wagers will still rely heavily upon time, patience and experience. Those who are able to leverage the best of both worlds should therefore perform quite well.

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How Artificial Intelligence is Improving the Online Sports Betting Experience - MWWire

PHOTO:Adobe

Last September, Gartner published its Hype Cycle for AI in which it identified two emerging trends (and five new AI solutions) that would have an impact on the workplace. One of those trends was what Gartner described as the democratization of AI. While there are many ways that this can be interpreted, in simple terms what it meansfor workersis the general distribution and use of AI across the digital workplace to achieve business goals.

In the enterprise, the target deployment of AI is now likely to include customers, business partners, business executives, salespeople, assembly line workers, application developers and IT operations professionals. As AI reaches a larger set of employees and partners, it requires new enterprise roles to deliver it to a wider audience.

While this was an emerging trend last summer, with COVID-19 and the adoption of many new technologies to enable remote working, the widespread use of AI, while still only anecdotal, now appears to be an established fact in the workplace.

Bill Galusha of senior director of marketing at Calsbad, Calif.-based digital intelligence company ABBYY points out, however, that this is not a new phenomena. In the past couple of years, weve seen AI enabling technology like OCR and machine learning become more accessible to non-technical employees and partners through no code/low code platforms, he said.

He points out that thetechnologies designed to help workers understand and extract insights from content have been in high demand as more digital workers increase the number of tasks a knowledge workers have to perform.

In practical terms these new AI platforms enable users to design cognitive skills that are can be easily trained to take unstructured data from type of document like invoices, utility bills, IDs, and contracts, or access trained cognitive skills available through online digital marketplaces. This new approach to making it easy to train machine learning content models and deliver them as skills in a marketplace are certainly going to fuel the online growth and reusability of AI as businesses look to automate all types of content-centric processes across the enterprise, he said.

Related Article:The Risks and Rewards of the Citizen Developer Approach

However, if AI is being used widely across the enterprise, it does not necessarily follow that it is providing business value to every organization, according to Chris Bergh, CEO of Cambridge, Mass.-based DataKitchen, a DataOps consultancy and platform provider.

AI is being deployed everywhere we look, but there is a problem that no one talks about. Machine learning tools are evolving to make it faster and less costly to develop AI systems. But deploying and maintaining these systems over time is getting exponentially more complex and expensive, he told us.

Data science teams are incurring enormous technical debt by deploying systems without the processes and tools to maintain, monitor and update them. Further, poor quality data sources create unplanned work and cause errors that invalidate results.

This is the heart of the problem and one that is likely to impact the bottom line of any business that uses AI. The AI code or model is a small fraction of what it takes to deploy and maintain a model successfully. This means that the delivery of a system that supports an AI model in an application context, is an order of magnitude more complex than the model itself. You can't manage the lifecycle complexity of AI systems with an army of programmers. The world changes too fast. Data constantly flows and models drift into ineffectiveness. The solution requires workflow automation, he said.

There is another problem for businesses too. Given the explosion in the amount of data that is available to them, at first glance you would think that developing AI was getting easier and, consequently, easier to deploy democratized across the enterprise. Not so, according to Chris Nicholson, CEO of San Francisco-based Pathmind, which develops a SaaS platform that enables businesses to apply reinforcement learning to real-world scenarios without data science expertise.

The real problem, he argues is that you cannot decouple algorithms from data, and the data is not being democratized, or made available, across the organization. In many cases, as with GDPR, the data is getting harder to access and because the data is not being democratized, most startups and companies will not be able to train AI models to perform well, because each team is limited to the data it can access.

In a few cases, a general-purpose machine-learning model, can be trained and made available behind an API. In this case, developers can build products on top of it, and that very particular type of AI is slowly percolating into products and impacting customers lives. But, in most cases, businesses have custom needs that can only be met by training on custom data, and custom data is expensive to collect, store, label and stream, he said. At best, AI is a feature. In the best companies, data scientists embed with developers to understand the ecosystem of the data and the code, and then they embed their algorithms in that flow.

Like the discussion around citizen data scientists (and democratizing data science), business leaders need to know what they want this new democratized AI to do. They will not be able to design and build AI models from scratch; that will always require an understanding of what the underlying methods and parameters do, which requires theoretical knowledge.

Given some gray box AI systems, one can envision such systems learning to solve well-defined classes of problems when they are trained or embedded by non-AI experts, Michael Berthold, Switzerland-based KNIME CEO and co-founder, said. Examples he cites are object recognition in images, speech recognition, or probably also quality control via noise and image tracking. Note that already here choosing the right data is critical so the resulting AI is not already biased by data selection.

I think this area will see growth, and if we consider this democratization of AI, then yes, it will grow, he added. But we will also see many instances where the semi-automated system fails to do what it is supposed to do because the task did not quite fit what it was designed to do, or the user fed it misleading information data.

It is possible to envision a shallower training enabling people to use and train such preconfigured AI systems without understanding all the algorithmic details. Kind of like following boarding instructions to fly on a plane vs. learning how to fly the plane itself.

If organizations take this path to develop AI, there are two ways enterprises can push AI to a broader audience. Simplify the tools and make them more intuitive, David Tareen, director of AI and analytics at Raleigh, N.C-based SAS told us.

Simplified Tools - A tool like conversational AI helps because it makes interacting with AI so much simpler. You do not have to build complex models but you can gain insights from your data by talking with your analytics.

Intuitive Tools - These tools should make AI easier to consume by everyone. This means taking your data and algorithms to the cloud to become cloud native. Becoming cloud native improves accessibility and reduces the cost of AI and analytics for all.

In organizations do this, they will see benefits everywhere. He cites the example of an insurance company that uses AI throughout the organization will reduce the cost of servicing claims, reduce the time to service claims, and improve customer satisfaction compared to the rest of the industry. He adds that some enterprise leaders are also surprised to learn that enabling AI across the enterprise itself involves more than the process itself. Often culture tweaks or an entire cultural change must accompany the process.

Leaders can practice transparency and good communication in their AI initiatives to address concerns, adjust the pace of change, and result in a successful completion of embedding AI and analytics for everyone, everywhere.

See the rest here:
Why Artificial Intelligence May Not Offer The Business Value You Think - CMSWire

The COVID-19 pandemic affected the entire world in some way or the other.  |  Photo Credit: iStock Images

In a rapidly advancing globalisation that has turned the entire Earth into one huge village, speedy connectivity and communication also ensured a rapid advance of the COVID-19 pandemic that began with a strain of the novel coronavirus that first emerged in Wuhan, China in late 2019. Now, as per a science paper published in Nature Communications, "The spread of influenza can be modelled and forecast using a machine-learning-based analysis of anonymized mobile phone data. The mobility map, presented in Nature Communications this week, is shown to accurately forecast the spread of influenza in New York City and Australia."

The year 2020 dawned with the world bracing to handle a possible crisis and by the end of the year, global deaths reached nearly 2 million.

To cut the long story short, mankind has now been through so much in terms of mental agony, pain, loss, death, long-lasting illnesses and economic downslide - all on account of this pandemic - despite rapid advances in science - that it has begun to dread the prediction by environmentalists and scientists that we have just entered a pandemic era and more such pandemics are likely to come.

Predicting the onset of a Pandemic:According to a report in the BBC, a team of scientists has used artificial intelligence (AI) to work out where the next novel coronavirus could emerge.

The researchers are reportedly putting to use a combination of learnings from fundamental biology and tools pertaining to machine learning.

This is not mere conjecture and the scientists are taking ahead of what they have gained from similar experiments in the past. Their computer algorithm predicted many more potential hosts of new virus strains that have previously been detected.The findings have been published in the journal Nature Communications.

According to this report in Nature Communications, the spread of viral diseases through a population is dependent on interactions between infected people and uninfected people. The Building-models that predict how the diseases will spread across a city or country currently make use of data that are sparse and imprecise, such as commuter surveys or internet search data.

Dr Marcus Blagrove, a virologist from the University of Liverpool, UK, who was involved in the study, emphasises the need to know where the next coronavirus might come from.

"One way they're generated is through recombination between two existing coronaviruses - so two viruses infect the same cell and they recombine into a 'daughter' virus that would be an entirely new strain."

Scientists say that to get the prediction algorithm right, the first step was to look for species that were able to harbour several viruses at once. Lead researcher Dr Maya Wardeh, who is also from the University of Liverpool, successfully deployed existing biological knowledge to teach the algorithm to search for patterns that made this more likely to happen.

We were able to predict which species had the chance for many coronaviruses to infect them... Either because they are very closely related (to a species known to carry a coronavirus) or because they share the same geographical space.

This step concluded that many more mammals were potential hosts for new coronaviruses than previous surveillance work - screening animals for viruses - had shown.

How could the findings be useful?One thing that seems to be widely accepted is the claim by scientists that COVID-19 is not the last pandemic we are seeing and that scientists believe another pandemic will happen during our lifetime.The scientists say their findings could help to target the surveillance for new diseases - possibly helping prevent the next pandemic before it starts. But the researchers warn against demonising the animal species. They point out that "spill-over" of viruses into human populations tends to be linked to human activities like wildlife trade, factory farming and keeping animals cooped up in unhygienic conditions.

"But it's virtually impossible to survey all animals all the time, so our approach enables prioritisation. It says these are the species to watch," the University of Liverpool researcher added.

The scientists say the "ideal" use of this technique would be to help find viruses as they're recombining.

"If we can find them before they get into humans," said Dr Blagrove. "Then we could work on developing drugs and vaccines and on stopping them from getting into humans in the first place."

As they say, forewarned is forearmed.

Link:
Liverpool scientists deploy Artificial Intelligence to develop model that predicts the next pandemic - Times Now

People put the intelligence in artificial intelligence

Is the digital workforce ready to take over? Well, not quite. Artificial intelligence may be capable of assuming many tasks, but it will be some time, if ever, that it could replace jobs on a widespread basis. It simply has too many limitations.

Instead, we need to acquaint a generation of workers with technologies to take on the more mundane, repetitive portions of their jobs, and in turn elevate their decision-making roles within enterprises. Thats the word from Steve Shwartz,AI author, researcher and investor, who points out that the notion of AI taking jobs is a myth. However, AI will have a profound impact on employment.

Shwartz, author of the just-published book Evil Robots, Killer Computers, and Other Myths: The Truth About AI and the Future of Humanity, points out that many people are concerned that intelligent robots will be able to read manuals, take courses, and eliminate all our jobs. Fortunately, this is science fiction.

Todays AI systems are only capable of learning functions that relate a set of inputs to a set of outputs, he says. This simple paradigm has enabled fantastic technological accomplishments such as facial recognition, language translation, and cars that can see and avoid pedestrians. However, these learned functions have no more intelligence than a function that translates Fahrenheit temperatures to Celsius temperatures.

It would take a huge breakthrough to create intelligent robots, and todays AI researchers have only vague ideas about how to create such a breakthrough, Shwartz says. Such a breakthrough is about as likely as time travel.

The bottom line is that any job that requires commonsense reasoning is safe; probably for our lifetimes. Maybe forever, he continues. People-oriented skills in finance, marketing, sales, and HR are probably safe. The types of jobs that will be impacted and not necessarily negative impacted are ones that involve repetitive decision-making that can be learned by AI systems.

Rather than replace jobs, AI is replacing tasks especially repetitive, data-oriented analyses are candidates for automation by AI systems. If it is possible to create a large training set of examples in which each example is labeled with the correct answer, that analysis can likely be learned by an AI system, says Shwartz.

Another task category that AI will enhance is repetitive customer service interactions, he continues. AI-based chatbots are assuming more customer-service work, and customer service jobs that involve a human following a script to interact with customers are at the most risk. Human interactions that require real, unscripted conversations are not at risk.

For non-technical careers, the greatest impact is the availability of massive amounts of data, Shwartz says. The field of marketing has already been transformed by data. Marketers analyze data from Google to determine which keywords to buy. They analyze huge amounts of customer data to determine which campaigns should be targeted to which customers. And they analyze massive databases of web traffic to determine what changes to make to their websites. Todays marketers need to be data analysts. Most companies are relying more and more on data to drive the business. Many formerly non-technical jobs now require extensive data analysis. Workers who do not adapt will be left behind.

While AI will be replacing many repetitive tasks and amplifying intelligence through data, the most exciting opportunities will be seen with the creation of new types of businesses. Shwartz was a founder of one of the first AI companies, Cognitive Systems, in 1986. As an angel investor, Shwartz now sees large numbers of startups whose business models are only possible because of AI technology: Computer-vision technology enables computers and robots to identify objects, faces, and activities. Startups are developing in-store products that identify customers and provide highly personalized offers direct to their smartphones. Companies are developing surveillance products for law enforcement and the military. Startups are creating AI-based medical applications to read MRIs and diagnose diseases. Other vendors are using other types of AI technology to detect fraud and stop cyber-attacks, analyze legal documents, predict the weather, improve search results, and even design golf clubs.

Along with achieving greater sophistication and better mimicking human reasoning, AI also brings additional challenges, Shwartz relates. Computer-vision systems have been shown to be biased against minorities. It is not only unethical for companies to roll out biased systems, but also bad for business. In Europe, due to GDPR regulations, it is illegal and similar regulations are almost certain to follow in the US. These biases are often created inadvertently using biased data. Ensuring systems are non-discriminatory can be harder than developing the technology in the first place.

Read more here:
Needed: People To Put The Intelligence In Artificial Intelligence - Forbes

AI can be leveraged to build efficient water plants and optimize water resources to reduce energy costs in the long run.

Artificial intelligence is disrupting industries with its wide range of capabilities including augmenting human intelligence and processing huge data chunks. There have been discussions and reports on sustainable AI which can work efficiently while conserving the environment. AI has also proved effective in renewable resources industries. Let us discuss the impact of AI in another sector the water sector. Water is an imperative need to live life and it has been going through pollution and scarcity for a long time. Climate change is a reality that can increase water stress in many places and increased water contamination will result in a huge water crisis which we are not yet ready to deal with. According to a report by UNICEF and WHO, 1 in 3 people globally does not have access to safe drinking water. This scenario is going to become grave in the coming years if we do not address the issue.

AI in water management might come off as a huge revelation but it can change the way we treat and manage water sources around us. Let us see how AI can impact the global water sector.

An India Today report states that it is estimated that around 40% of piped water in India is lost to leakage. According to a US EPA report, an average family can waste 180 gallons of water per week, or 9400 gallons of water annually, from household leaks, which is equivalent to the amount of water needed to wash more than 300 loads of laundry.

We waste a lot of water through leakages, burst pipes, etc. and AI and IoT can help reduce this wastage. Implementing AI to analyze real-time water loss and automating pipes to shut off whenever there is a leak can improve the amount of water wastage. AI can predict leaks in storage tanks and help in mending them before it is too late. Devices connected through IoT can communicate better and integrate various systems across a city or place.

AI can be used to reduce pollutants in the water which in turn decreases water contamination and scarcity of clean water. AI can be leveraged to detect the amount and composition of toxic contaminants since AI works on optics, which can increase the efficiency of waste management systems. Water quality can be continuously monitored and it is possible to get real-time data on the quality through machine learning and big data. Neural networks and IoT will reduce the energy costs which otherwise increases when using conventional methods.

AI can make the process of water management easier with data analytics, regression models, and algorithms. These cutting-edge technologies help in building efficient water systems and networks. AI can be used to build water plants and to get the status of water resources. Water managers and government bodies can use AI to build a smart water system that can build efficient infrastructure for water management and can adapt to changing conditions. These systems will be cost-effective and sustainable that can optimize all water management solutions and predict potential damages.

Agriculture is the biggest water-using sector and many lands use a good portion of groundwater for irrigation purposes. Smart Irrigation will leverage AI systems to minimize the use of water and also optimize the water resources without wastage. AI systems can detect the groundwater levels and also estimate the agricultural needs to balance the usage of water by guiding sprinkler systems.

More developed precision-based AI systems can predict the weather conditions, climate, and humidity to enable better management of agriculture. The smart farms will be able to reduce leakages and analyze the soil to determine the condition of plants and their water needs using AI sensors.

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The Promise of Artificial Intelligence in Water Management - Analytics Insight