Later in 1956, American computer scientist John McCarthy organised the Dartmouth Conference, where the term ‘Artificial Intelligence’ was first used.
Today, AI is a much broader term and refers to a range of technologies from Automation to Deep Learning.
#2 AI can replace the human brain – completely
Humans have evolved over millions of years from being hunter-gatherers to agricultural societies to a modern-day man who can succeed in secondary and tertiary industries. We have adapted, evolved and became good at surviving in the real world. Despite this many people hold an opinion that AI will replace the driving force of one of the most complex machines on this planet – the human brain.
AI has clearly come a long way. Its ability to learn vast amounts of data, recognise patterns, and produce results is improving us in countless ways. However, the problem with achieving true AI is also its greatest strength – that it does not learn like a human. The technology behind AI is scientific and complex and building a competitive AI from scratch requires expensive specialised talent. For instance, a successful image recognition solution is more accurate than most humans, but the same coding cannot address another type of problem.
AI cannot replace a complex structure of neurons and humans will continue to use their intelligence for more innovation. Humans do and will continue to, play a major role in most AI applications, especially critical ones in research and medicine. Each of our innovations has made the race more productive, and that is what AI will further add to the human race.
#3 AI poses a threat to security & privacy
While the benefits of AI and Big Data technologies are being felt, people also consider them as a threat to their anonymity and privacy.
With online social accounts, digital identities and other digital data gathering entities – both private and government – privacy has become a pertinent question. With the emergence of sophisticated AI systems, these privacy concerns have been aggravated. AI brings the ability to fetch, combine, and analyse a huge quantity of data from varied sources. The impression is that AI can perform these designated operations with no supervision and there is fear that humans will lose control of a system entirely. Instances of data and privacy breaches heighten this fear.
In reality, AI technologies are being utilised to create a safer and more secure society. AI brings speed, scale, and automation to computing and is changing the way we work, live, and interact. We are guiding AI capabilities for better healthcare provision, citizen safety, research accuracy, and even enhanced cybersecurity. Very often, the data used by these algorithms are aggregated and anonymised.
#4 AI will replace jobs
There is an abundance of fear, uncertainty, and doubts about the risk and opportunity of AI. Will it create jobs or destroy them?
There is no doubt that AI is poised to transform jobs and will change the face of employment. It is easier to see existing jobs disrupted by new technology than to envision what new jobs the technology will enable. AI is poised to replace tasks, not jobs. Some functions – and sometimes all the functions – of an individual or team might be automated. Employees with no plans or desire to re-skill should be concerned, but those who are continuously improving and changing their skill sets need not be too concerned that automation will put them in an unemployment queue.
“While businesses will face pain, as they adjust to new lower cost and higher productivity expectations – and employees will need to continually update their skills – the overall assessment is for jobs growth. It is just that the jobs created will be different to the jobs that exist today”, as Tim Sheedy (Principal Analyst, AI & Automation, Ecosystm) puts it in his report Automation Will Transform Jobs – Plan for Change Now
The NAB Cloud Guild is a good example of how organisations should provide training to not just technology staff but to any interested employee, on emerging technologies to equip their business for future demands.
#5 AI is implemented only by large vendors
AI is driving many Digital Transformation (DX) projects and large vendors, especially with platform and enterprise capabilities, have had the first movers’ advantage in AI deployments. Businesses are striving to make their systems more intelligent for better process automation and customer retention. After 40 years of automating manual tasks using enterprise applications (such as ERP, SCM, and CRM), intelligent systems will make many of these systems redundant – or at the least reduce business reliance on them.
One of the big challenges for large businesses – and their IT teams – today is to customise their AI to their organisations’ DX requirements. Many companies have made their first foray into the world of AI – often starting with technologies such as RPA, IoT sensor analytics, and chatbots. They are now looking to go beyond evolving their RPA solutions into Smart Process Automation (SPA) solutions. They are also going beyond basic chatbots/ virtual assistants to implement NLG and semantic computing, as their customer focus deepens. For these large enterprises, integration of AI solutions with internal systems and other AI solutions is the key challenge, and they often prefer to partner with their existent enterprise vendor or systems integrator for their AI implementations.
However, smaller organisations and start-ups are equally leveraging AI. Several tech start-ups also exclusively focus on AI and are developing a niche, industry-specific solutions. These smaller solution providers will probably be integrated into larger vendors’ partner ecosystems, as their capabilities deepen, and their customer base grows. Organisations need not look to only larger, established vendors for their AI implementations.
AI is still an emerging technology and it might take some time for AI to be trusted. The truth, however, is that AI opens up immense possibilities for individuals, enterprises, and governments.
Do the supposed threats outweigh the benefits of AI? We would very much love to hear your suggestions, ideas, and thoughts on this subject.
I recently attended a briefing with Ramco Systems – if you haven’t heard of them, they are one of an emerging group of software vendors who are challenging the big application software companies – SAP and Oracle. They put innovation at the centre of their business – aiming to constantly drive improvement for their customers, and bringing companies the benefits of systems that consumers see in their web-based and mobile apps but have been sorely missing from the enterprise application market. To be honest they are a breath of fresh air in a market that needs it – and their endeavours are seeing results both in plaudits from analyst firms and new customer wins.
At the briefing, Ramco demonstrated some of the AI capabilities they have been weaving into their software platforms. And in doing so they have shown the gap between today’s systems and systems that actually work for their clients. ERP, HR, Payroll and other enterprise applications are data sinks – they demand constant input, and while they do a good job in automating business processes, they could do so much more.
Within Ramco they have moved away from email completely for employee inquiries – all interactions now happen with their transactional chatbot, including scheduling meetings, checking leave balances, discovering and understanding personal achievements, raising a travel request and claiming travel expenses – as well as understanding company policies and supporting employees with speculative queries. This same bot is available for clients as they aim towards a zero-UI interface – no more logging onto systems and interrogating applications, running searches. Now you ask a question and get an answer – using an IM client or a voice interface (such as Google Home or Amazon Alexa devices). This is the way systems should serve employees.
Like other enterprise application vendors, they have added an AI capability to their platform – but they are taking the extra step to make that AI work out of the box (or the cloud). For example, with all the information in your HR systems (employee skills, time and attendance, incentives, expenses, payroll) they are looking at making that information accessible and actionable for potential users – creating systems that understand the context and anticipate needs.
In your finance or ordering systems, they are applying machine learning so it understands that ‘client A’ tends to order specific items from specific locations – so ordering agents are guided towards those options versus having to scroll through long lists.
(see images for an example of that in the process)
They are recommending where costs should be allocated or validating inputs based on historical learnings. The systems can catch a mistake, errors or even fraud – saving the business significant amounts of money and of time in error correction or re-work.
Ramco’s vision is that agents only have to manage exceptions in enterprise applications – not every single detail. Complete automation is still an unrealistic expectation, but businesses should aim for 85% automation, with 12% of processes needing intervention for mild intervention and 3% needing deep intervention. In Ecosystm’s experience speaking to businesses that have automated to such a degree, an 85% automation does NOT lead to an 85% saving – as you typically automate the easier cases anyway. But the savings should be real and measurable – up to 50% time saving for accounts receivable or payable teams, for payroll teams, for help desks or for other highly manual processes should be achievable.
And while the business case can be built on the saving, the pay-off also comes in happier and more engaged employees who have the information right at their fingertips to make better business decisions or drive smarter business processes.
So why highlight Ramco’s AI capabilities? For a number of reasons:
For AI to be widely adopted, it needs to be easy and accessible – Many other vendors (the big cloud players in particular) are making AI tools and assets available for customers, but they still have to do the hard work – find a business problem, gather the data, train the algorithm, deploy the algorithm and then train users on the new process. There are hundreds – or even thousands of examples of processes in business that can be made smarter and easier through the use of machine learning and AI – and vendors should be building these capabilities into the products and platforms. Ramco is doing that – they are by no means alone – but they are a good example of a software vendor that is disrupting a market by focusing on helping their customer succeed.
I believe there is a bigger trend going on in the way businesses buy software (and look out for an upcoming report on this topic). More and more I see businesses adopt the best solution for their needs – NOT the one that does 80% of what they want. And the best software is often built by smaller, more agile companies. They build for specific business needs and specific niches – and they focus on providing exactly what customers want. I am seeing a general move away from the big platform providers towards the smaller ISVs. Partly because they cost less (I regularly hear companies say they saved up to 90% by using a specialist provider!) – but also because they provide the best solution – and businesses can no longer compromise when it comes to driving the best customer and employee experiences. Again, Ramco is a part of this change.
You should demand more from your applications provider– an AI platform is not enough. They need to make your actual application smart – they need to be able to automate processes you are already doing. If you have data the system should be able to learn, they need to focus on making the system work for you, your employees and your customers – not the other way around (as is too often the case). AI needs to be a core component of your business applications, not a bolt-on.
AI – Machine Learning. AI and machine learning make it possible for insurance companies to mine both structured and unstructured data. The use cases range from underwriting, claims management and personalised offerings through behavioural data and sentiment analysis. There are examples of early adopters in the auto industry – but again there are obvious and wider use cases, that can benefit risk modelling, pricing, customer acquisition, and agent and channel efficiency.
AI – Virtual assistants/Chatbots. This falls right in with managing customer experiences. As customers expect more self-service (yes, the future will see less agents!) several insurance providers are using chatbots at several customer touchpoints, covering departments such as Sales and Claims. This will increasingly be the norm as smart phone (and app) penetration increases and the target base becomes younger. There are online-only insuranceproviders where clients interact with chatbots services and they are able to cater to a larger, untapped, mass market. There are more advanced adoption examples such as USAA’s use of intelligent personal assistant equipped with an NLP engine that have been trained with a deeper knowledge of policies. Virtual insurance agents will become more of a norm in the near future.
Which brings us to the important question on how insurance companies are planning to leverage InsureTech. Multiple stakeholders could benefit from InsureTech adoption. The Claims department appears to be a key stakeholder, focused both on fraud prevention and automation when it comes to transaction and processing. Sales and Customer Service appear to be next in line, where personalisation of product offerings would equip the teams better for a competitive market.
Challenges of AI Adoption in Insurance
It is obvious that the insurance companies are still at a nascent stage of adoption of AI and InsureTech. While cybersecurity is a recurrent concern (as it should be), it is a common concern across any technology area. The biggest challenge that the insurance industry faces in adoption of AI and other data-driven technologies is the actual data management – from access to integration. The industry may be data-intensive, but the data exists in silos. In the end an InsureTech implementation should benefit multiple departments – Underwriting, Claims, Sales and so on.
Several insurance companies will look to consulting firms and systems integrators to create a roadmap to their transformation journey and enable the data integration – especially as technologies evolve and when internal IT lack the right skills to manage these projects.
The technology that will be the key component of InsureTech and transform the insurance industry is AI. In spite of the challenges of adoption, the industry will be forced to transform to survive in the highly competitive market. Companies in emerging economies will especially benefit from investing in AI – in fact, India and especially China will see a surge in InsureTech investments.
This situation is only exacerbated by social media and the prevalence of “fake news” that can quickly propagate incorrect, unscientific or unsubstantiated rumours.
As AI is evolving, it is raising some new ethical and legal questions. AI works by analysing data that is fed into it and draws conclusions based on what it has learned or been trained to do. Though it has many benefits, it may pose a threat to humans, data privacy, and the potential outcomes of the decisions. To curb the chances of such outcomes, organisations and policymakers are crafting recommendations about ensuring the responsible and ethical use of AI. In addition, governments are also taking initiatives to take it a step further and working on the development of principles, drafting laws and regulations. Tech developers are also trying to self-regulate their AI capabilities.
The goal of the councils is to work on a global level around new technology policy guidance, best policy practices, strategic guidelines and to help regulate technology under six domains – AI, precision medicine, autonomous driving, mobility, IoT, and blockchain. There is participation of over 200 industry leaders from organisations such as Microsoft, Qualcomm, Uber, Dana-Farber, European Union, Chinese Academy of Medical Sciences and the World Bank, to address the concerns around absence of clear unified guidelines.
Similarly, the Organization for Economic Co-operation and Development (OECD) created a global reference point for AI adoption principles and recommendations for governments of countries across the world. The OECD AI principlesare called “values-based principles,” and are clearly envisioned to endorse AI “that is innovative and trustworthy and that respects human rights and democratic values.”
Likewise, in April, the European Union published a set of guidelineson how companies and governments should develop ethical applications of AI to address the issues that might affect society as we integrate AI into sectors like healthcare, education, and consumer technology.
“Before an organisation embarks on the project, it is vital for a regulation to be in place right from the beginning of the project. This enables the vendor and the organisation to reach a common goal and understanding of what is ethical and right. With such practices in place bias, breach of confidentiality and ethics can be avoided” says Ecosystm Analyst, Audrey William. “Apart from working with the AI vendor and a service provider or systems integrator, it is highly recommended that the organisation consult a specialist such as Foundation for Responsible Robotics, Data & Society, AI Ethics Labthat help look into the parameters of ethics and bias before the project deployment.”
Another challenge arises from a data protection perspective because AI models are fed with data sets for their training and learning. This data is often obtained from usage history and data tracking that may compromise an individual’s identity. The use of this information may lead to a breach of user rights and privacy which may leave an organisation facing consequences around legal prosecutions, governance, and ethics.
One other area that is not looked into is racial and gender bias. Phone manufacturers have been criticised in the past on matters of racial and gender bias, when the least errors in identification occur with light-skinned males. This opened conversations on how the technology works on people of different races and genders.
San Francisco recently banned the use of facial recognition by the police and other agencies, proposing that the technology may pose a serious threat to civil liberties. “Implementing AI technologies such as facial recognition solution means organisations have to ensure that there are no racial bias and discrimination issues. Any inaccuracy or glitches in the data may tend to make the machines untrustworthy” saysWilliam.
Given what we know about existing AI systems, we should be very concerned that the possibilities of technology breaching humanitarian laws, are more likely than not.
Could strong governance restrict the development and implementation of AI?
The disruptive potential of AI poses looming risks around ethics, transparency, and security, hence the need for greater governance. AI will be used safely only once governance and policies have been framed, mandating its use.
William thinks that, “AI deployments have positive implications on creating better applications in health, autonomous driving, smart cities, and a eventually a better society. Worrying too much about regulations will impede the development of AI. A fine line has to be drawn between the development of AI and ensuring that the development does not cross the boundaries of ethics, transparency, and fairness.”
While AI as a technology has a way to go before it matures, at the moment it is the responsibility of both organisations and governments to strike a balance between technology development and use, and regulations and frameworks in the best interest of citizens and civil liberties.
The SmartLaw Guild brings together case studies from the legal industry and organises knowledge sharing sessions. Speaking at the launch of SmartLaw Guild, Communications and Information Minister S Iswaran, said that the majority of legal practices in Singapore are catered to the SME sectorgiven that 90% of organisations in Singapore fall under the category. The Government is making an effort in the evolution of technology to support the SME legal practices. Mr. Iswaran also encouraged practicing lawyers to take advantage of the skills training provided by the IMDA’s Techskills Accelerator initiative in areas such as cybersecurity, AI and data science.
Commenting on the announcement, Ecosystm VP & General Counsel, Nandini Navale said “Across jurisdictions, law firms are bound to licensing and regulatory conditions and have to follow strict standards of professional ethics, confidentiality, and care to clients. This could be a possible reason for their ‘abundantly cautious’ approach towards the adoption of new technology and digitalisation. A glitch or even a minor fault in the technology could result in the loss of license to practise, breach of regulatory obligations, reputational damage or can compromise the interest/privacy of clients. Therefore, AI and technology in systems and processes will have to be proven reliable and fail-safe as a condition for the implementation in the legal sector.”
Law has been a conservative industry. This is fast changing, however with the “BigLaw” in countries investing heavily in technology and looking to implement AI to help their legal staff perform due diligence and research, provide additional legal insights and in process automation in legal work.
Advanced technology solutions powered by AI are enhancing business capabilitiesand the adoption of AI in the legal industry can help in a quicker resolution of disputes and more consistent outcomes. “AI is capable of transforming the legal sector. The technology could be used to sift through volumes of case law and litigation history, and help lawyers to interpret, prepare and support their positions. Legal issues spotters are being utilised in the contract due diligence and review, legal-tech being deployed for routine and low-value work. Applications for time trackers, billing and invoicing, and legal data analytics are also being adopted” says Navale “The Singapore Government is indeed walking the talk – an example of this is the introduction of the Venture Capital Investment Model Agreements (VIMA) documentation.” The initiative was launched in 2018 by the Singapore Academy of Law (SAL) and the Singapore Venture Capital & Private Equity Association (SVCA) which comprises a set of standard documents that improve the process of structuring a deal and transactions for venture capital firms, start-ups, and SMEs. The core working group for the initiative adopted technology and created a questionnaire that guides through the documentation with auto-versioning and customisation to save time, cost and effort.
How have some Disruptive Technologies Impacted the Legal Industry?
NEC has taken this into consideration and published a set of principles for the application of biometrics and AI. The “NEC Group AI and Human Rights Principles” will guide the company along the lines of privacy and human rights. These initiatives were led by the Digital Trust Business Strategy Division, in collaboration with several other divisions within the company, as well as industry stakeholders including industry experts and non-profit organisations.
In the year 2016, I considered Rio as the first Internet of Things (IoT) Olympic games in my article “The future of “The Internet ofOlympic Games”. In Rio, we saw how athletes, coaches, judges, fans, stadiums, and cities benefited from IoT technology and solutions which transformed the way we see and experience sports. Next year we will have another opportunity to validate my predictions for the upcoming Tokyo 2020 Summer Olympics. Therefore, we may designate Tokyo as the first Artificial Intelligent (AI) Olympic Games.
During my presentation at the University of Dubai, I explained to the audience how incredible IoT and AI technologies are and to what extent they are impacting our sports experience. I elaborated on IoT and AI’s significant role in health management, improving aptitude, coaching, and training. These technologies are enabling athletes to improve performance, coaching for better preparation, fewer judgment errors, and a better experience for spectators. I also commented on the importance of IoT and AI to enhance the security of teams, audience, stadium, and cities altogether.
With the use of IoT and AI we are creating a world of smart things transforming sports business where every thousandth part of a second is crucial to predict the outcomes of a race, a match or a bet. I cited various examples on how different sports are utilising IoT and AI, and not in the least I shared a vision of the future that’s like 10-15 years onwards from the present – Can you envision a world of a real and virtual world of sports integrated together? Can you visualise robots and humans or super-humans playing together?
On the other side, speaking of the challenges involved with AI, IoT, and machine learning models for sporting, I conveyed the dark side of these technologies. We cannot forget the fact that the sports industry is a market and therefore enterprises, Governments, and individuals may make erroneous uses of these technologies.
In summary, it in this session I shared my point of view on-
How IoT and AI will transform coaches, athletes, judges, and fans.
How IoT and AI will attract the audience to the stadiums
How IoT and AI will transform the Industry?
How AI is changing the future of sports betting?
How IoT and AI will transform athletes, coaches, judges and fans?
While the true essence of a sport still lies in the talent and perseverance of athletes, it is often no longer enough. Therefore, athletes will continue to demand increasingly sophisticated technologies and cutting-edge training techniques to improve performance. For example, we may see biomechanical machine learning models of players to predict and prevent potential career-threatening physical and mental injuries or can even detect early signs of fatigue or stress-induced injuries. It can also be used to estimate players’ market values to make the right offers while acquiring new talent.
Coaches are consuming AI to identify patterns in opponents’ tactics, strengths and weaknesses while preparing for games. This helps coaches to devise detailed game plans based on their assessment of the opposition and maximise the likelihood of victory. In many leading teams, AI systems are used to constantly analyse the stream of data collected by wearables to identify the signs that are indicative of players developing musculoskeletal or cardiovascular problems. This will enable teams to maintain their most valuable assets in prime condition through long competitive seasons.
We tend to think that technology is helping us to make decisions in sports more accurate and justified. That´s why we look at the inventions such as from Paul Hawkins – creator of Hawk-Eye, a technology that is now an integral part of the spectator’s experience when watching sport live or more recently VAR in soccer.
The use of technology is allowing the decision makers to experience the game with multiple cameras angles in real-time combined with the aggregated data from various sensors (stadiums, things, and athletes) thus making them make more objective and accurate decisions.
We as spectators or fans need more transparency about the exercise’s difficulty, degree of compliance and final score. And we have the technology to do it.
The IoT and AI technology don’t claim to be infallible – just very, very reliable and judges also need to be adapted to new technologies.
Without fans, sports would find it difficult to exist. It is understandable companies are also targeting fans with IoT and AI to keep them engaged whether in the stadium or at home.
How IoT and AI will attract the audience to the stadiums?
The stadiums, sports clubs and many leagues across the globe are incorporating technologies both inside and outside the stadium areas to boost the unique experiences for fans and not only during the gameplay.
The challenge is how to combine the latest technologies with old-school stuff to please supporters from both newer and older gen. people looking forward to witnessing a game in a stadium?
How will the stadiums of the future be? I read numerous initiatives of big clubs and leagues, but I am excited about the future stadium of Real Madrid. I wish the club would allow me to advise them how to create a smart intelligent Global environment to provide each fan with an individual experience, know who is in the crowd, learn fan behaviors to anticipate their needs.
How IoT and AI will transform the Industry?
“As long as sports remain a fascination for the masses, businesses will always have the opportunity to profit from it. As long as there is profiting to be gained from the world of sports, the investment in and incorporation of technology for sports will continue.”
I went through an article warning about an entirely new world order that is being formed right now. The author explained how 9 companies are responsible for the future of AI. Three of the companies are Chinese (Baidu, Alibaba, and Tencent, often collectively referred to as BAT), while the other six are American (Google, Amazon, IBM, Facebook, Apple, and Microsoft, often referred as the G.Mafia). The reason is obvious, as far as AI is about optimisation using the data that’s available, these 9 companies will manage most of the sports data generated in the world.
Collaboration is needed now to stop this threat and to address the democratisation of AI in sports. It is important that companies and Governments around the globe work together to create guiding principles for the development and use of AI and not only in Sports. This means we need regulations but in a different way. We do not want AI power to lie only in a handful of lawmakers, renowned and smart people who lack skills in IoT and AI.
Will AI change the future of sports betting?
The impact of technology on sports cannot be specifically measured, but some technological innovations do raise questions about fairness. Are we still comparing apples with apples? Is it right to compare the speed of an athlete wearing high-tech running shoes to one without?
Whether we like it or not, technology will continue to enhance the athlete’s performance. And at some point, we will have to put specific rules and regulations in place about which tech enhancements are allowed.
There is a downside to advanced technology being introduced to sports. Nowadays, Machine Learning models are routinely used to predict the results of games. Sports betting is a competitive world itself among fans, but AI can substantially tilt that playing field.
I am afraid that IoT and AI companies may spoil the result predictions but more concerned about the manipulation of competitiveness that AI algorithms could bring with the Terabytes of data collected with IoT devices and other sources like social media networks, without the permission of the users.
The sports industry is already generating billions of dollars every year and without control and awareness, we could find the future generation of ludopaths and a small number of service providers controlling the game.
Let me know what else would you like to see in my future posts. Leave your comments below.