Artificial Intelligence as a Service – AIaaS

Artificial Intelligence as a Service (AIaaS) – Suddenly, it’s everywhere. Are you AI-ready for adopting AI and its bundle of technologies, especially neural networks? If not, then be ready to be read in history books. Different AI provider platforms offer a number of styles of machine learning and AI. Let’s correct our perception (if it’s negative). Artificial intelligence is not here to take or kill our jobs; it’s here to create new jobs with much higher skills and eliminate mundane task-based jobs. We don’t need to worry, but we need to upgrade and be ready.

Artificial Intelligence & People

AIaaS are we not missing the fact that artificial intelligence is about the people, not the machines. Technology and non-technology companies are now investing and bringing out the real and materialistic values of Artificial Intelligence to the real world. It’s almost after a frustrating and hard work of the decade.

AI has started delivering value. Using the contemporary view of computing exemplified by recent models and results from non-uniform complexity theory has proven this fact. Blockchain technology is being used to distribute the financial market and not keep it under the control of a few major companies.

How about the same concept with artificial intelligence so that it can deliver the values in a plug-and-play mode, i.e., as a service technique? Any new startup if need or wants can just plug and play this service to get benefits rather than leaving it just a few big hands.

Investment in artificial intelligence is growing fast. Tech giants of AIaaS service providers like Google, Microsoft, Apple, and Baidu, known for their dominance in digital technologies globally, are spending a couple of tens of billions of dollars on AI, with 90% of this spent on R&D and deployment and 10% on AI acquisitions.

AI Development across the Globe

It takes money to make money, and now a lot of that money is going into the development of artificial intelligence. Any intelligence level surpassing human intelligence is called the superintelligence level, which is still 50 years or more ahead.

Machine learning, as an enabling technology, received the largest share of both internal and external investment. In short, get AI ready or ready to be swapped by AI very soon. Before getting into deeper with this amazing term, i.e., artificial intelligence, let’s glance through it from the helicopter view perspective.

Anything that needs information processing in the human mind is human intelligence, and if the same task is performed by machine, it can be termed artificial intelligence, like looking at pictures and telling if this person is sad or happy, decent or nasty.

The animal picture is of a dog, cat, etc. AI doesn’t need to demystify anything more, as its metaheuristic approach has almost overshadowed legacy technologies and the computational power of cognitive systems techniques.

Tiny Plastic/Metal Pieces with AI Power

We are all most likely living in the era of the most expounding time, where we are so fortunate to see such a huge transformation of technology from large mainframes to PCs to the cloud, from huge bulky telephone handsets to tiny pieces of plastic or metal in our pockets, starting from a few megabytes of memory to a few petabytes, and also witnessing what is coming our way in years to come. Alan Turing, an early computer scientist, threw a very simple test known as the “Turning Test” about artificial intelligence.

In the Turning Test, he proposed a simple test to decide whether a computer had achieved true artificial intelligence only by giving the same question to a human and a computer. If the interrogator couldn’t tell which response came from the computer and which came from the person, then the system has attained artificial intelligence.

AI Glossary – AIaaS

AI is huge, and some times it can also be very confusing and used interchangeably. We may not be able to define all of them here, but let’s try to name a few. Artificial Intelligence, Machine Learning, Deep Learning, Supervised and Unsupervised Learning, Cognitive Computing, or just a prettier way to say “artificial intelligence”.

• Artificial neural networks, often referred to as neural networks, neural nets, or deep neural nets, are computer systems modeled after the structure and functioning of living brains, designed to process information and learn from data.
• While bots or chatbots may not fall directly under the umbrella of artificial intelligence, concepts such as data mining, data science, and big data play integral roles in AI development and application.

Artificial intelligence is everywhere in the media, its hype is not hidden. Recent comments from few billionaires about AI (some commented in favour some commented little conservatively) are all over on social media but leaders and drivers of this force must look past the hype and understand how to apply it to create real business value now.

AI – Still Far From The Reality But Not A Myth Anymore

Separating AI as a myth from reality can be tough at this time, but in the future, it will come in handy and get easier through the hype and hyperbole that surrounds artificial intelligence. It relates to job elimination perception, a great high-speed northbound journey of automation, and an intense push for people to upgrade their skills to be in the race. We may have been looking at AI all wrong for some time.

• Contrary to common belief, artificial intelligence encompasses more than just robots; it includes bots, AI subsets, and agents like Siri, all of which are continually advancing in intelligence through black box technology.
• AI is poised to permeate virtually every aspect of our lives, appearing in various forms across products and services we use daily. However, it’s crucial to remain vigilant against the hidden risks associated with blindly trusting AI’s “black box” capabilities.
• With the advent of AI as a service, offerings such as machine learning (ML), natural language processing (NLP), Language Understanding Intelligent Service (LUIS), and deep learning (DL) have become increasingly accessible. The dream of creating human-like thinking machines, once thought unattainable, is now a reality embedded in the smartphones we carry everywhere.

Will it be wise to ignore this for some time? Yes, but that does not mean we can dismiss AI as overhyped and pay it no attention at all. The impact of AI technologies will be significant when applied in the form of augmented intelligence.

Financial Business innovation & AI

Business and financial innovation is starting to focus on two things above all: artificial intelligence and blockchain. Further, the effects that artificial intelligence and its subcategories, i.e., artificial neural networks, deep learning, and machine learning, estimate are all of a definite, palpable, empirically ascertainable quality; they are such pleasures, fascinating things, business cost reductions, and pains (for some billionaires as they fight with other billionaires) as most of the industries across the globe can feel.

All can observe the presence of AI in such a strong way that all its technological or moral inferences are open enough at every point to the test of practical experience. Statistical inquiries are the biggest enabler for machine learning as the incidence of taxation or of particular taxes, though ideal or even about equality of a palpable arithmetical kind is practically unattainable by governments, are not altogether to be put aside.

AI will help and do good for humanity. This kind of revolution tells us where we are headed. If you’re waiting for AI, look around you—you will find some form of artificial intelligence at work almost everywhere. Though its pinnacle will take around 50 or more years, machine consciousness is indomitable and will happen sooner or later. PayPal is using deep learning as a best-in-class approach to block fraudulent payments and has cut its false-alarm rate in half.

AI promises benefits but also poses urgent challenges (not threats, please make a note) that cut across almost all industries and businesses, be they of any nature, i.e., software development, technical support, customer care, medicines, the law domain, or factory or manufacturing work. The need of the hour is to upgrade our skill sets to exploit AI and not compete with it.

Artificial Intelligence As A Transformative Technology

Please note: I am a big fan, follower, and lover of everything about AI, which can bring only the best things as just good enough. Accenture recently published research showing that AI has the potential to boost corporate profitability by an average of 38 percent and lead to an economic boost of $US14 trillion across 16 industries in 12 countries by 2035.

In the 1940s and 1950s, a number of researchers explored the connection between neurology, information technology theory, and cybernetics. In today’s time, setting up a hub for AI development or R&D is not a difficult task as long as funding for research work is available. Global competition to attract AI talent and investment, and progress will need to be made on the ethical, legal, and regulatory challenges that could otherwise hold back AI.

AI at the core of the business of financial services can be put to better use in customer-experience functions. As such businesses can expand AI adoption swiftly in response to the economic and industrial invasion and acquire more data, laggards will find it harder to catch up. AI adoption is meeting its trepidation despite an overall acceptance of AI; nearly two-thirds of global consumers register concern about AI, although mostly at a moderate level. Top concerns revolve around security and job loss.

• The evolution of AI from science fiction to a tangible business tool reflects its transformative impact on various industries.
• Despite past perceptions, AI has transitioned from being perceived as solely for PhD programs or researchers to becoming an essential aspect of predictive analysis, empowering businesses to prepare for an uncertain future with more informed decisions.

This is not to say, however, that human beings cannot expect and influence the future or futures, but to do so with any degree of accuracy, assumptions about future crises, needs, and consequences must be based on a realistic assessment of human nature and behavior, verifiable history, and our present conditions.

Impact of AI on confidentiality, integrity and availability

Small companies will be empowered by AI, and large enterprises will become more responsive to their customers. Artificial intelligence is the only technology that is going directly from disappointment to deadly without being beneficial in between. The lemmatization of artificial intelligence has many names, i.e., narrow, strong, general, or super, as well as its subsets, i.e., machine learning, machine intelligence, machine consciousness, deep learning, etc.

Can we really stop this storm of artificial intelligence? The answer is definitely no, as innovations have no stop button. Can we regulate it? The answer is yes, for sure. Regulation can define boundaries and limit the usage level of AI in each domain of today’s work. Additionally, there are related fields of artificial intelligence that study intelligent methods that also learn from data and their environment. Examples include computational intelligence and metaheuristics.

Artificial intelligence can do a lot of things, but it also has its limitations. We need to test machine learning tools to see what they can really do instead of just hoping they will work. This is important to keep us safe. AI used to be just a dream, but now it has become a really good thing for businesses. For a business, it’s not possible to avoid this.

• Poor decision-making can have detrimental effects on businesses, potentially leading to notorious failures, as witnessed in the smartphone industry.
• Artificial intelligence offers the potential for businesses to enhance their communication capabilities, facilitating better conversations and interactions with customers. It’s crucial for organizations to explore and integrate AI into their operations to stay competitive in an increasingly AI-driven landscape.

How to use AI is entirely up to us. When artificial intelligence was just science fiction, nobody thought it would become a real force in the business world. Even more growth by adding new subsets to its core, i.e., data science, operations, customer success, and sales.

Required Business Capabilities

AI will continue to effectively serve existing markets and businesses while at the same time always keeping an eye out for new ones and speeding up its overall pace of innovation and disruption. It’s very interesting to see how fast this pace is evolving. In 2009, trending companies were those who said, “There’s an app for that or this”. In 2016, the same companies said, “There’s a bot for that” The list of companies and industries benefiting from AI is growing by the day, in addition to the various applications of machine learning.

The best AI solutions for financial companies like banks and insurance companies will likely be ones with strong and smart financial systems. These solutions should have good data security and powerful analytics features. Machine learning is still not advanced enough to be very helpful in this field. In the world of banking and technology, companies will use AI in two ways: to make smart decisions and to automate tasks.

Financial technology companies will focus on both of these areas to try and get ahead of their competition. In the future, AI will allow people to do more important things and create more jobs. Ultimately, AI will create a strong advantage for companies that have faith and confidence in it. We will make a really good offer that will benefit our customers, people who use our product, and people who invest in us all at the same time.

Modern mobile payment infrastructure availability: Africa has payment instruments such as mobile wallets for merchant payments, bill payments, prepaid airtime top-ups, etc. Smart machines produce smart payments with in-built payment intelligence. High-powered algorithms are not a new phenomenon in finance, though, and for this industry, the name of the game is efficiency and precision, which suit FinTech more due to their fast-adopting nature and risk appetite.

Food For Thoughts

Artificial intelligence is a broad and active area of research, but it’s no longer the sole province of academics; increasingly, companies are incorporating AI into their products. AI is controlled by humans, and I wish that in the long term it should stay the same.

• It is super important to ensure that AI remains under human control and does not evolve into an uncontrollable force.
• Baidu’s remarkable advancements in speech-to-text technology, outperforming humans in similar tasks, underscore the rapid progress of AI capabilities.
• Amazon’s utilization of deep learning algorithms for product recommendations showcases the potential for AI to enhance customer experiences and drive business growth.

How AI will be transforming the future of finTech to elaborate items from the above list in African markets and opportunities are even more dramatic – In just the past five years.

Conclusion: – it’s evident that AI is a technology that is becoming increasingly integrated into our lives and businesses. Like all future products, AI will inevitably find its place in our daily routines and industries. While Silicon Valley embraces AI as the next big thing, there is skepticism on Wall Street and other sectors. It’s worth noting that, so far, only intelligence has been artificial, and the risks associated with AI are real and natural. Natural language generation has the ability to create, write, and narrate business stories, yet it still faces challenges when confronted with risks. As we continue to navigate the ever-evolving landscape of AI, it’s important to remain vigilant and address potential risks while embracing the opportunities it presents.

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Feedback & Further Questions

Besides life lessons, I do write-ups on technology, which is my profession. Do you have any burning questions about big data, AI and ML, blockchain, and FinTech, or any questions about the basics of theoretical physics, which is my passion, or about photography or Fujifilm (SLRs or lenses)? which is my avocation. Please feel free to ask your question either by leaving a comment or by sending me an email. I will do my best to quench your curiosity.

Points to Note:

It’s time to figure out when to use which “deep learning algorithm”—a tricky decision that can really only be tackled with a combination of experience and the type of problem in hand. So if you think you’ve got the right answer, take a bow and collect your credits! And don’t worry if you don’t get it right in the first attempt.

Books Referred & Other material referred

• Open Internet research, news portals and white papers reading
• Lab and hands-on experience of  @AILabPage (Self-taught learners group) members.
• Self-Learning through Live Webinars, Conferences, Lectures, and Seminars, and AI Talkshows

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