You’ve heard about Artificial Intelligence but can it really be used in the casino business? After all, doesn’t the casino already have a house edge?

However, even before the pandemic, major casinos which cost millions of dollars to operate were experiencing stiff competition. Several casinos in Atlantic City were closed, and many Las Vegas casinos were on a tight financial rope.

Now that the pandemic has happened, and casinos throughout both Europe and the US are facing fiscal challenges, many casino operators are looking to Artificial Intelligence to ensure the casino remains in the black.

What is Artificial Intelligence?

Artificial Intelligence is a branch of computer science that uses computers to accomplish tasks that would normally require human intelligence.

Of course, when we talk about computers accomplishing tasks that mimic human intelligence, we’re not talking about simple tasks like exchanging 5 dimes and two quarters to make a dollar’s worth of change, or a vending machine sending 20 cents in change after you buy a bag of peanuts.

No, Artificial Intelligence is used in manufacturing robots, creating smart chat boxes for websites, and operating as a virtual travel assistant, and a thousand other tasks.

So how are casinos using Artificial Intelligence?

One thing is for sure and that is the gambling games will remain the same. Playing blackjack will still be playing blackjack. New Zealand casinos pokies will still be the one-armed bandits they have always been, with of course some progress in technology that comes with time.

However, one of the cardinal rules of casino managers is that it is significantly easier and more profitable to target a return gambler than it is to court a never seen before gambler.

What’s the solution? Artificial Intelligence to the rescue.

The single biggest use of Artificial Intelligence is to provide a database of players, what their preferred method of gambling is, the amount of money they spend on various games as well as how much time they spend in casino gambling.

Naysayers will say that is impossible to do but remember, there are eyes in the sky everywhere at physical casinos. By using artificial intelligence to survey casino security footage, plus information used on casino player cards, casinos have more information than ever before.

Using AI tools such as predictive analysis, the casino’s marketing department is more armed than ever before.

The object, of course, is to be able to analyze who are the most valuable players to the casino. And while a lot of popular lore centers on ways that the casino attracts high-value “whales” to gamble at the casino, the AI focus is on the more affluent younger generation.

Why is younger better?

One thing learned from the pandemic is that older gamblers are fickle while younger gamblers in their mid-20s to 30s are easier to attract.

For the next few years at least, casino managers have decided that younger gamblers are their bread and butter.

Using Artificial Intelligence, marketing departments are very eager to attract these younger gamblers for a return trip. In addition, the major players in the casino staff are paying attention to what facilities and shows attract the younger crowd.

Customer service is greatly improved where AI is involved and is very useful in areas such as nearly instantaneous check-in to avoid long 2-hour waits.

Security

Security is also greatly enhanced using Artificial Intelligence.

The use of facial recognition prevents gamblers who have been banned by the casino for activities such as card-counting at Blackjack from returning, and potential trouble makers are also screened out.

Conclusion

It seems that Artificial Intelligence is going to be a big part of everyone’s lives, and casinos are no exception. AI can be very useful not only for the casino but to provide a better experience for the player.

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After a long day at work, you are dazed but at the same time determined to watch the newest episodes of your favourite series. And just when you’re in the nail-biting scene, the video has paused, then begins to buffer and never resumes! After a few frustrating seconds later, the video is at its lowest resolution, hardly showing clarity.

How did an exhilarating affair end into an arduous one?

Say Hello, to Video Streaming challenges. Honestly, This is not how a video should be streaming. That is when the potential game-changers like AI and Deep Learning comes into action. They can ensure Zero-delay, High Definition, enhanced streaming OTT making the user experience more enjoyable and worthy.

Adaptive BitRate

OTT or Over the Top content makers, as well as the providers, are launching intriguing content every single week. But the Quality of experience which is generally referred to as QoE is one main issue that’s not been able to tackle well enough by the OTT platforms.

QoE is about a video’s overall resolution, startup time, stalls and pauses that happen through the relay. Seamless delivery of video is highly reliable on internet speed or the so-called bandwidth which can drastically affect the user experience. That is when this ABR comes into play tackling the challenges with an innovative algorithm.

The parameters of a video have been “adapted” dynamically by the performance of your network. Therefore, compromising the quality of the video. But, it is still dependent on internet speed. It is 2021, and a lot of talks is happening around Deep Learning.

Intelligent Solutions

Videos have been the new type of data communication, literally an extension to an email or text. This has created a necessity for developments in video streaming with optimization of network and data. Most of the happening these days are unperceptive to the content it holds. The bitrate of a scene where the characters are just having a conversation differs from that of an action scene. Imagine seeing the wrinkles on Dumbledore’s face when he raises his wand, but unable to see clearly when Harry is fighting Voldemort.

And that needs an answer to the following questions

• What if a scene’s content could be comprehended?
• What if streaming algorithms learn the QoE by every scene?
• What if the encoding is instructed with relative importance to all frames?
• What if the streaming is friendly on any device?

The best possible answer is Deep Learning.

Deep Learning

Artificial Intelligence and Deep Learning are advancing technologies to improve video content and user’s QoE. Content conscious AI can remarkably improve the viewing experience making it personalized, immersive and novel. DNN (Deep Neutral Network) can be made device-aware, dynamically computing it with the strategic resources to scale up the performance of streaming.

An intelligent and notable method to enhance QoE is when the encoder looks at the entire video than looking at the pixels in individual frames. This helps for

• A better understanding of content
• Delivering the highest quality stream
• Identifying the content redundancies.

How can the impact of Deep Learning take place?

Device Aware

The device quality plays a major role in the entire process of decoding. Making it device friendly by all means, says that people can watch it anytime, anywhere and by any device. A click on the app of their smartphones. Because smartphones are the new laptops. Having the track on which device is targeted by the user can make his/her personal experience most engaging. The unique potential of a device can be utilized as its soul ability to redefine the entire streaming.

Context-Aware 

It doesn’t end there at being device-aware. Deep Learning and Artificial Intelligence can be made to pay specific attention to the entire context of the video, search preferences, genre interest. For instance, a user is streaming a particular series every week. Deep learning can use this data to show the user the preference show, the similar genres, other shows from the director and cast. This not only improves the QoE of the user but also makes it a Win-Win for the OTT providers as well.

Understanding the business of Streaming  

One might wonder why an OTT platform has to invest in such user engaging technologies? The answer to this is a recent survey done across 1000 customers who are using the OTT streaming in Urban India. As many as 62% of people viewing through the OTT face issues like buffering, pausing and hang of the app whilst used on travelling.

More than ever, In recent times, the customers are making it clear by immediate feedbacks on official and social media sites regarding any dissatisfaction While using the OTT. This shows a clear intolerance in regards to lower QoE which can affect the revenue. Long load times, buffering, pixelation and stalled videos should no longer be seen to sustain this business.

The emerging trends, the pandemic situation has given new dynamics to online streaming. The limitations to short films or fiction videos have widened up to live stream of sports, gaming and releasing movies. Thereby generating more revenue.

Gone were the days of gaming with Personal Computers. But, games like PUBG having pulled greater crowds just from a smartphone. The new gaming platform from Google, stadia gave an announcement stating as low bandwidth as 25mbps is enough for exceptional streaming.

This tells us how cost-effective ideas are emerging to change the phase of this business using AI and deep learning. A recent survey showed that 53.98% of viewers were willing to upgrade to premium subscriptions for intriguing content that has minimal streaming challenges.

Finally, The big picture of delivering high-quality videos at lower cost subscriptions to generate greater revenues is in view. The considerable benefits obtained from the usage of AI and the impact of deep learning in creating a greater streaming experience has solved many practical conditions. The biggest innovations of all this process are DNN based advancements. So, very soon this is going to change all the demographics to matchless QoE and incredible video consumption.

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Well, is artificial intelligence a job-killer or not? We keep hearing both sides, from projections of doom for many professions that will necessitate things such as universal basic income to help sidelined workers, to projections of countless unfilled jobs needed to build and manage AI-powered enterprises. For a worker losing his or her job to automation, knowing that an AI programming job is being created elsewhere is of little solace.

Perhaps the reality will be somewhere in between. An MIT report released at the end of last year states recent fears about AI leading to mass unemployment are unlikely to be realized. “Instead, we believe that—like all previous labor-saving technologies—AI will enable new industries to emerge, creating more new jobs than are lost to the technology,” the report’s authors, led by Thomas Malone, director of the MIT Center for Collective Intelligence, conclude. “But we see a significant need for governments and other parts of society to help smooth this transition, especially for the individuals whose old jobs are disrupted and who cannot easily find new ones.”

The future of AI and job growth — or losses — may be nuanced, a recent report from BCG and Faethm suggests. “Though these technologies will eliminate some jobs, they will create many others,” the report’s team of authors, led by BCG’s Rainer Strack. “Governments, companies, and individuals all need to understand these shifts when they plan for the future.”

What needs to be understood? For starters, “the net number of jobs lost or gained is an artificially simple metric to gauge the impact of digitization,” Strack and his co-authors state. “For example, eliminating 10 million jobs and creating 10 million new jobs would appear to have negligible impact. In fact, however, doing so would represent a huge economic disruption for the country—not to mention for the millions of people with their jobs at stake.”

There’s even a paradox in play. Computers tend to perform well in tasks that humans find difficult or time-consuming to do, “but they tend to work less effectively in tasks that humans find easy to do,” the report notes. Also, in many areas, technologies “will improve the quality of work that humans do by allowing them to focus on more strategic, value-creating, and personally rewarding tasks.”

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In other words, AI can’t take over many of the soft skills essential to businesses growth — initiative, intuition, passion, and ability to sell ideas and concepts. Add that to more technical abilities needed to build and maintain AI and digital environments — and keep them focused on what the business needs. “In many sectors, severe shortages of skilled workers will mean that growth in demand for talent will be unmet,” Strack and his co-authors state. “This is particularly true for computer-related occupations and jobs in science, technology, engineering, and math, since technology is fueling the rise of automation across all industries. This is why the computer and mathematics job family group is likely to suffer by far the greatest worker deficits.”

At the same time, there will also be increasing demand for jobs requiring compassionate human contact, such as health care, social services, and teaching, they add.

Along with the BCG-Faethm’s observations, it should be noted that AI cannot replicate the entrepreneurial skills that will be pulling together technology solutions and platforms to connect to the needs of markets. Humans are the innovators.

What to do? Strack and his team urge people to take charge of their professional development through lifelong learning. “Individuals will have to take greater responsibility for their own professional development, whether that means through upskilling or reskilling,” they state. “Pay attention to sources of information and update skills accordingly, either by searching out high-quality providers of education or by charting your own course amid the vast amount of online-learning offers.”

The BCG-Faethm team also makes the following recommendations from a corporate perspective:

• Think strategically in terms of hiring. “A company should regularly assess the current size, composition, and development of its workforce. It should also evaluate future demand on the basis of strategic direction and determine the gaps for certain jobs and specific skills.”
• Support upskilling and reskilling. “Given the rapid shifts in skill requirements and the number of entirely new tasks and roles that are emerging, the labor market will be unable to supply sufficient new talent to fill available positions. Companies therefore need to supplement external hiring with internal development initiatives and on-the-job training.”
• Create a lifelong learning culture. “Corporate training used to consist of certifications or intermittent training programs, but the digital economy will demand a constant upgrading of skills. Content and skill upgrades should be delivered in a variety of formats so that they can be integrated into the daily routine of every employee, ensuring a nimble and agile workforce.”
• Rethink talent recruitment and retention strategies. “Companies may need to shift the recruitment focus from hiring for skill to hiring for will: as some of the skills needed in the future (such as coding computer languages) will most likely be self-taught or come without an explicit certification, HR professionals will need to view candidate criteria with a more open mind and embrace diverse curricula. Companies may also opt to create an employee pool to which people with new skills can be added without yet knowing which field of operations they’d be best suited for.”

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MediaNama held an online discussion on the impact of data policies on Artificial Intelligence (AI) on January 28, 2021. We have summarised the key issues and concerns raised during the discussion in a report

Given the rapid commercial and government deployments of AI technologies, we felt it was imperative to discuss the impact of AI development on rights and privacy. This is crucial particularly in context of upcoming regulations in India on usage of data i.e. the proposed Personal Data Protection Act and a framework for Non Personal Data. 

We spoke to experts in the lending industry about how they collect data and use AI to generate insights about borrowers, and the broader utility of using data and AI for real-world decision making. We discussed the privacy implications of using AI in the context of proposed government regulations, and further explored the relationship between data and the development of AI, the impact of AI on data governance, fairness, accountability, transparency of algorithms, and how anonymisation, inferred data, and consent would interact with usage of data for AI.

The discussion was hosted with support from Facebook, Microsoft, and Flipkart. The Centre for Internet and Society was a community partner for the sessions. We livestreamed the entire discussion, catch it here:

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Analyttica Datalab is a technology-enabled analytical solutions company that drives business impact through a strong focus on its customers. The company offers an optimal man-machine blend of solutions that combine analytical expertise and technology and can repeatably be used by enterprises at significantly lower cost.

The company was founded by Rajeev Baphna, a veteran in the data and technology space, having worked with Citigroup for 16 years. Analyttica is basically around platforms, tools, technologies, and people. There are certain challenges in the tech ecosystem in relation to tools, technology and people, which led to Analyttica being founded. The first challenge is when you look at Big Data, AI and ML, there’s no holistic way to be able to define a business problem, solve it, extract the solution and run it at scale in a business. These components are addressed by 10-15 different tools and technologies. The second challenge is the ability for a business’s ROI to be visible right away. And the third challenge is talent, as the way people use these technologies and tools are still very process-oriented, and the ability to apply in a business context is still left to a human being. Analyttica was formed to be able to create solutions to these problems. They have also patented their approach in the United States.

The organisation has two primary products they take pride in. These products are built around the ‘learn, apply, and solve’ concepts. Analyttica TreasureHunt® (ATH) SimuLab is used by enterprises to create a long-term and sustainable data culture. Analyttica TreasureHunt® (ATH) Precision creates value in terms of solving the business problems highly rapidly and with 100 percent accuracy.

The ATH SimuLab product helps organisations scale and become AI-ready faster. It’s not restrictive in nature. The learners or the talent focuses on applying themselves contextually within an environment that simulates real life scenarios and do not need to be distracted by the coding aspects while doing so.

With ATH Precision, they are trying to develop a holistic analytical ecosystem for organisations, where they can connect to their data and different applications and contextualise their business problems, instead of just randomly running some AI or ML algorithms. As a team, they can collaborate and are able to solve the problems. They can institutionalise their learning and the experiences they gain along the way.

The organisation believes that you need to have a strong blend of subject matter expertise, business analytics exposure and technology, as well as a culture that fosters collaboration.

Their workplace has an agile design, so that everybody is accessible to each other all the time and they frequently huddle together to solve problems.

Developing the product was not a cakewalk for them initially on a technology perspective, but things slowly improved. They base ATH as a set of microservices and they have Docker-based deployment. They’ve also leveraged a lot of mature open source libraries and tools. The incremental and iterative approach helped the team to accelerate their learning faster.

The hierarchy at Analyttica is pretty flat and stands apart from other data and analytics companies. Most of their employees previously worked in bigger organisations with expansive processes, which also came with silos and boundaries. At Analyttica, those boundaries don’t exist, and individuals get exposure to diverse fields. They follow the vision to learn, apply and solve internally as well. Almost all meetings are an open door. Those who are hungry to learn and grow are given access to more challenges that they can help solve. Even amidst all the impactful work they do, Analyttica is able to maintain a fun-loving atmosphere, with games and potlucks, and the team is like a large family.

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This spring, regulators in Wyoming were scrambling to figure out how to process 10,000 applications for oil and gas permits filed since oil prices began to rise in early 2016. At the same time, new permits were streaming in at an unprecedented rate, raising concerns that some proposed energy projects would not get the scrutiny – or public input – they warranted.

It was exactly the kind of problem people in the artificial intelligence community love to solve; it just hadn’t been considered for environmental permitting before.

Besides making life easier for regulators, machine learning could help watchdog groups and local residents better understand what risks new energy projects pose to the environment and public health.

A new AI application

I decided to run the idea by some acquaintances in the Bay Area tech community, one of whom had recently used Natural Language Processing to sort through thousands of public comments on the controversial XL Pipeline project.

What if we can use trained algorithms to also process oil and gas filings and extract information and themes that might otherwise fall through the cracks, I asked them.

It could turbo-charge our analysis of applications filed under the National Environmental Policy Act and help us catch impacts before they happen. Other kinds of development proposals could benefit, too, were the application to catch on.

Two people – a programmer and graduate student at Stanford University, and the XL Pipeline researcher, also at Stanford – were immediately excited about the idea and agreed to help me get started.

Scrub government websites? The project takes shape

Environmental Defense Fund laid the groundwork for the AI project earlier this year, planning to use a trained computer program to assess future impacts on greater sage-grouse habitat in the western United States.

It quickly became clear that using artificial intelligence to analyze energy development projects could bring a new level of transparency to today’s outdated and overwhelmed permitting process. By combining software and algorithms trained to “scrub” data from government websites, critical and potentially overlooked filings can be located, downloaded and processed in a matter of hours.

It would also solve another challenge: a lack of standard formats for permit applications that make such documents hard to identify and flag potential problems with NEPA filings that might otherwise be missed.

Future impacts of projects – such as pollution of a groundwater basin or habitat loss for an imperiled animal – could be identified and compiled on a spreadsheet, registry or an online map for everyone to see.

For that, you also need resources.

Grueling work, big gains

Getting artificial intelligence treatment of NEPA filings off the ground is a question of manpower, plain and simple. That also explains why progress with our initial greater sage-grouse project has been slow.

It requires weeks of data entry to train algorithms, on top of high-level math and data processing skills – neither of which your typical watchdog group or local government office possesses. But if we stop and consider what we’d gain – a grasp of what’s really happening in America’s rapidly developing rural areas – we may see just how much such advances could be worth.

So I wasn’t surprised to get a call the other week from an entrepreneur on the East Coast who had heard about our project. He proposed a different approach to the problem, which would feed in reams of permit applications and then extract information we care about based on some simple criteria.

Exploring these methodologies to pull from deep wells of data could circumvent the time-consuming work of training initial algorithms and save an immense amount of time and resources. We think we’re onto something potentially big.

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If you’re like most people, calling an insurance company isn’t among your favorite activities. That’s because the insurance industry is one of the least innovative areas for customer experience, meaning that customers typically come away from their interactions disappointed and dissatisfied. However, things are definitely changing, and artificial intelligence is playing a large role. The fast-growing technology has the potential to disrupt the entire industry and greatly improve the insurance customer experience.

Artificial Intelligence In The Claims Process

The insurance agency is notorious for its outdated processes. Filing a claim often looks the same today as it did decades ago because the industry isn’t consistently leveraging new technologies that are available to them. If an employee is busy or on vacation, a claims request could sit still until the right person is back. The outdated processes make it harder for agents by increasing the workload and forcing them to work with antiquated systems and frustrated customers.

However, AI can be applied to improve the claims process. Claims currently are touched by multiple employees. However, a new process of “touchless” claims doesn’t require any human intervention. This process uses artificial intelligence and other technology to report the claim, capture damage, audit the system, and communicate with the customer. The potential here is huge, as the process could allow clients the chance to file claims without having to wade through red tape.

Companies that have already automated some aspects of their claims process have seen a significant reduction in processing times and quality. AI-powered claims could also fight against one of the most costly elements of the insurance industry: fraudulent claims, which cost the industry more than $40 billion a year. Instead of relying on humans to manually comb through reports to catch inaccurate claims, AI algorithms can identify patterns in the data and recognize when something is fraudulent.

Future Of AI And Insurance

The industry is definitely ripe for AI disruption. Customers expect to be able to interact with companies through modern technology; a recent survey found that 74% of consumers say they would be happy to get computer-generated insurance advice.

Many insurance companies are already using artificial intelligence to some degree, and the number of companies following in their footsteps is sure to increase dramatically over the coming years. Artificial Intelligence has never been less expensive or more accessible, which means most companies don’t have a reason not to adopt it in at least some form.

Chatbots

Chatbots work through messaging apps many customers already have on their phones, which makes them a natural next step in customer interaction. In order to truly be effective, chatbots must have natural language processing and sentiment analysis so they can understand what customers are really asking. Effective chatbots can process concerns that are either typed or spoken from customers and provide personalized service. In the insurance space, chatbots can be used to answer basic questions and resolve claims, as well as sell products, address leads, or make sure customers are properly covered by their insurance.

Marketing And Underwriting

Insurance is a competitive market, so a strong marketing strategy is vital. Traditionally, insurance companies used blanket methods like cold calling customers, but today’s customers expect personalized sales tactics. AI can pull in customer data to create a full profile that can be used to offer only relevant insurance products and remember a customer’s preferences.

Instead of spending valuable time and money on the underwriting process, which typically includes invasive questions and surveys about to dictate premiums, Artificial Intelligence could automate the entire process. Bots could potentially scan a customer’s social profile to gather information and find trends and patterns. For example, someone who has a healthy lifestyle and a steady job may be able to be connected to being a safer driver, which could lower insurance premiums. AI can analyze data better than humans to more accurately predict each customer’s risk, thereby providing customers with the right amount of insurance and companies with protection from risky customers.

Data

Insurance is driven by data, and it has a huge effect on the company’s bottom line and the satisfaction of the customer. A recent study found that nearly 80% of insurance executives believe artificial intelligence will revolutionize the way insurers gain information from their customers, with more than half saying the biggest benefit is being able to leverage better data for improved insights into the customers.

Telematics, or wireless communication of data back to an organization, is expected to be a huge area of growth for insurance. Many insurance companies already offer discounts to customers who transmit their driving data back to the company. Telematics and artificial intelligence can take this one step further by recognizing GPS patterns with the data, inferring road and traffic conditions, and even predicting and helping avoid accidents, which could potentially lead to fewer claims to process and safer and more satisfied customers.

The insurance industry has long been bogged down by outdated practices. However, the combination of a new wave of thinking and newly developed artificial intelligence technology has the potential to completely change the customer experience to provide great service in a way that resonates with modern customers.

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