5 Misconceptions about Artificial Intelligence

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Innovation is often fuelled by the evolution of technology, which unlocks greater potential for businesses. Artificial Intelligence (AI) is often considered the trendiest of today’s emerging technologies, viewed as the real enabler of innovation. Many businesses are adopting AI and investing in AI-based solutions. However, every emerging technology faces the same stumbling block – trust. AI has been no different and despite the growing adoption, not everyone is ready to jump on the AI bandwagon yet.

The other major stumbling block for AI has been the inability of the common man to grasp the implications of the technology and differentiate it from what has been depicted in Science Fiction and dystopian novels. There is also confusion in the mind of technologists on the definition of AI. Some experts will say that only Deep Learning with non-linear algorithms is AI, while most vendors promote their automation tools like AI. A few misconceptions have hence arisen regarding AI, its uses and its impact on the workforce and society.

 

#1 AI is a new technology

Despite recent hype around the technology, AI is not a new technology and not a product of this century’s innovations. The beginnings of AI can be traced to the middle of the 20th century and then it gained pace. During the second world war, an English mathematician and computer scientist, Alan Turing documented his ideas on creating an intelligent machine. Turing’s test theory proposed that if a machine could engage in full conversation with no detectable variances from a human, the machine could be deemed as a thinking machine. Turing worked to crack the German military’s encryption, the ‘Enigma’ code.

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.

AI-Timeline

 

#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.

This fear may be unfounded. Today, AI systems can simplify user privacy policies on websites, which most of the users do not bother to read and simply click on the ‘Accept’ box. Polisis is an AI-powered automated analysis tool for privacy policies. Polisis pulls a website’s privacy policy and takes around 30 seconds to interpret it, display a summary, and present a flowchart of the policy with highlights on how the online service will handle user data. An AI-powered chatbot called PriBot, answers questions about the privacy policy of any company.

AI Generated Visualisation
Polisis’ AI-generated visualization of the privacy policy for Pokemon Go. Image: PRIBOT

 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

Read Report – https://www.ecosystm360.com/#/link?type=report&id=155b38c6-3764-4715-a9ad-1ec6447260ac

A few businesses today are creating Automation Teams or Centres of Excellence – banks, telecommunications providers and utilities are leading this push. With continued effort, AI will eventually become intelligent enough to understand the tasks and make them easier for the workforce. Employees need to trust, use and maximise the full potential of the technology, and see its benefits for scaled implementation.

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.

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VendorSphere: Motorola’s Vision in the World of Critical Communications

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Critical Communications World, 2019 – TCCA’s largest event in global public safety communication – was held in Kuala Lumpur in June.  Mission-critical communications are essential to maintaining safety and security across a range from daily operations to extreme events including disaster recovery. A UN report estimated that economic losses from natural disasters could reach USD 160 billion annually by 2030.

I attended the event as a guest of Motorola Solutions – one of the leaders in this field. Many people associate Motorola only with phones not knowing that they have been the cornerstone of some of the largest critical communications deployments around the globe. For instance, Victoria Police completed its AUD 50M+ rollout of Motorola Solutions managed services, enabling almost 10,000 police officers across Victoria access to mobile devices loaded with smart apps, and data when and where they need it most.

Motorola’s ability to provide customers with a private network which is secure, robust and redundant in the event of disaster has also been one of the reasons for their success in the industry. In the event of natural disasters or terrorist attacks, situations can arise where networks will not be available to send and transport any information. Having a secure and private network is critical. That explains why some of the largest police departments in Asia work with Motorola and these include Singapore, Malaysia and Indonesia.x

Motorola acquired Australian mobile application developer Gridstone in 2016 and Avigilon, an advanced video surveillance and analytics provider in 2018. These acquisitions demonstrate how Motorola is innovating in the areas of software, video analytics and AI.

Key Takeaways:

 

Public Safety Moving to a Collaborative Platform with AI and Machine Learning 

Andrew Sinclair, Global Software Chief for Motorola Solutions sees AI enhancing future command and control centres and allowing greater analytics of emergency calls.  Call histories and transcriptions, the incident management stack, community engagement data and post incidence reporting are all important elements for command and control centres. Using AI to sieve through the information will empower the operator with the right data and to make the right on-the-spot decisions.

The Avigilon acquisition, enhances Motorola’s AI capabilities and less time is spent monitoring videos, giving first responders more time to do their jobs.  The AI technology can make “sense” of the information by using natural language technology. For example, if asked to find a child in a red t-shirt, the cameras can detect the child and also create a fingerprint of the child. The solution enables faster incidence detection by using an edge computing platform. It gathers the information and processes it to relevant agencies making the search operation faster and more streamlined. The application of AI in the video monitoring space is still in its early days and the potential ahead for this technology is enormous.

The other area that can empower first responders better are voice activated devices. The popularity of Alexa and Echo in the consumer world will see greater innovation in the application of public safety solutions. For example, police officers responding to an emergency may have very little time to look at screens or attend to other applications that need touching or pressing of a button as time and attention is essential is such scenarios. The application of voice activated devices will be critical for easing the job of the police officer on the ground. This will not only save administrative work on activities such as transcription, but also help in creating better accounts of the actual happenings for potential court proceedings.

While it is still early days for a full-fledged AR deployment in public safety, there are potential use cases. For example, firemen standing outside a building to make sense of the surrounding area could use AR to send information back to the command and control centres.

The Growth of Cloud-driven Collaboration

Seng Heng Chuah, VP for Motorola APJ talked about the importance of all agencies in public safety to be more open and collaborative. For instance, currently most ambulance, police and fire departments work in silos and have their own apps and legacy systems.  To achieve the Smart City or Safe City concept, collaborating and sharing information on one common platform will be key. He talked about the “Home Team” concept that the Singapore Government has achieved. Allowing all agencies to collaborate and share information will mean the ability to make faster decisions during a catastrophe. Making “sense” of the IoT, voice and video data will be important areas of innovation. Normally when a disaster happens, operators at command and control centres – as well as onsite staff – face elevated stress levels and accurate information can help alleviate that.

The move towards the public cloud is also becoming more relevant for agencies. In the past there was resistance and it was always about having the data on their own premises. In recent years more public safety agencies are embracing the cloud. When you have vast amounts of data from video, IoT devices and other data sources, it becomes expensive for public safety agencies to store the data on premise.  Seng Heng talked about how public safety agencies are starting to “trust’’ the cloud more now.  According to him, Microsoft has done a good job in working with local governments around the world, and their government clouds have many layers of certifications as well as a strong data centre footprint in countries. The collaboration between agencies and more importantly agencies embracing the cloud will drive greater efficiency in analysing, transcribing and storing the data.

The Rise of Outcome-based, Services-led Opportunities

Steve Crutchfield, VP of Motorola Solutions for ANZ, talked about how Motorola is a services-led business in the ANZ market. 45% of Motorola’s business in ANZ is comprised of managed services. The ANZ region is unique as it is seen as early adopters and innovators around public safety implementations. Organisations approach Motorola for the outcomes. Police and Ambulance for example in the state of Victoria use their services on a consumption model. Customers across Mining, Transportation, and Emergency Services want an end-to-end solution across the network, voice, video and analytics.

The need for a private and secure network is significant in several industries. In the mines, safety is of priority and as soon as the radio goes down it impacts productivity and when production stops that can results in huge losses for the mines. Hence the need for a reliable private network that is secure for the transportation of voice and video communication is critical.

Crutchfield talked about how the partner ecosystem is evolving with Motorola working with partners such as Telstra and Orion but increasingly looking for specialised line of business partners and data aggregation partners. Motorola works with 55 channel partners in the region.

Ecosystm Comment:

Motorola Solutions is an established player in providing an end-to-end solution in the critical communications segment. The company is innovating in the areas of software and services coupled with the application of AI. Dr Mahesh Saptharishi, CTO at Motorola Solutions talked about how AI will eventually evolve into “muscle memory”. That will mean that there is far greater “automatic’’ intelligence in helping the first responders make critical decisions when faced with a tough situation.

In the end the efficacy of critical communications solutions will not just be the technology stack, but the desire and ability for cross-agency collaboration.  As public safety agencies analyse large volumes of data sets from the network right to the applications, they will have to embrace the cloud, and which will help them achieve scale and security when storing information in the cloud. From the discussions, it was clear that the public safety agencies have started acknowledging the need to do so and we can expect that shift to happen soon.

Motorola will need to keep evolving their channel partner model and start partnering with new providers that can help in delivering some of the end-to-end capabilities across Mobility, AI, software, analytics and IoT. Many of their traditional partners may not be able to be that provider as the company evolves into driving end-to-end intelligent data services for their clients. The company is playing in a unique space with very few competitors that can offer the breadth and depth of critical communications solutions.

 

 

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Is AI Subsuming IoT?

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Some of you may be familiar with the famous Goya painting, ‘Saturn Devouring His Son ‘, which belongs to his series of ‘Black Paintings’. It is the best comparison I can make after returning from the TechXLR8IoT World Europe Summit in London.

In the painting we see the god Cronos/Saturn, who immutably governs the course of time, devouring one of his sons. I see Cronos as Artificial Intelligence (AI) and his son as the Internet of Things (IoT). The analogy can be carried further – there are other brothers waiting their turn to be devoured by this hungry father. Soon it will be Augmented Reality /Virtual Reality (AR/VR), Blockchain and Digital Twins.

If we look at the Ecosystm global IoT Study, we find that adopters of IoT are developing their capabilities in related technologies, with AI, Machine Learning and Predictive Analytics being the most significant. Very soon the IoT that is part of our lives will have AI embedded in them.

So, if you are still waiting for the IoT boom, this event is a confirmation that IoT is not throwing up many new things at least in Europe. The few IoT companies that exhibited their products and services at Excel London showed nothing that could overshadow the big winner, the ubiquitous father AI.

I have been finding it more difficult to justify coming to these IoT events. However, my role as a speaker and moderator allows me to maintain my influence and keep my followers on social networks, informed. The organisation this year has sought speakers that mix vendor presentations with success stories of clients. But this year neither of them was able to raise the tone of the event. The few large IT firms present such as Microsoft, SAP and Oracle are on the AI bandwagon and their demos on pure-play IoT are oft-repeated.

The larger systems integrators did not have adequate presence either. Many of them should have implemented IoT solutions for years but never really risked investing in IoT, and continue to focus on digitalisation projects, cloud migration projects, products updates and customised developments.

The discussions of the first years of the IoT boom revolved around connectivity, security, IoT platforms, and even business models. Now, nobody is interested in these matters anymore.

There was no significant IoT news during the event. Perhaps the most important announcement was made by Marc Overton who took advantage of his presentation to announce the recent collaboration agreement between Sierra Wireless and Microsoft to claim industry’s first full-stack IoT offerings.

As for my sessions, they mixed IoT and Blockchain, something that would have guaranteed success for attendees two years ago or even last year but that did not arouse great enthusiasm this year. It is evident that both technologies are becoming a commodity. Something that is not bad, since we would stop speculating about possible use cases and actually implement the technology in our lives and businesses.

Do not worry, the life of IoT events continues, and so this week there are three more just in Europe:

Here is what I think event organisers and Tech vendors should keep in mind:

  • Organisers need to find a way to facilitate meetings between vendors and attendants – and focus on how to create indirect lead generation opportunities. This would be mutually beneficial for all concerned.
  • Organisers and exhibitors need to try to reinvent these IoT events where we see IoT present in every corner of the floor, in every stage, in every service (cafeteria, rest rooms, transportation….). We need to breath IoT every minute.
  • IoT vendors need to demonstrate that they are working with partners and not present isolated use cases or demos. We need to see that “intelligent things” from different vendors in the exhibition area are interconnected.

Otherwise the IoT events will continue to drive away both visitors and exhibitors. What would you like to get out of future IoT events? Let me know.

 

3
Global Initiatives to Support AI Governance and Ethics

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Any new technology that changes our businesses or society for the better often has a potential dark side that is viewed with suspicion and mistrust. The media, especially on the Internet, is eager to prey on our fears and invoke a dystopian future where technology has gotten out of control or is used for nefarious purposes. For examples of how technology can be used in an unexpected and unethical manner, one can look at science fiction movies, Artificial Intelligence (AI) vs AI chatbots conversations, autonomous killer robots, facial recognition for mass surveillance or the writings of Sci-Fi authors such as Isaac Asimov and Iain M. Banks that portrays a grim use of technology.

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.

Amit Gupta, CEO, Ecosystm interviewed Matt Pollins, Partner of renowned law firm CMS where they discussed the implementation of regulations for AI.

To maximise the benefits of science and technology for the society, in May 2019, World Economic Forum  (WEF) – an independent international organisation for Public-Private Cooperation – announced the formation of six separate fourth industrial revolution councils in San Francisco.

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 principles are 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 guidelines on 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.

The Personal Data Protection Commission (PDPC) in Singapore presented the first edition of a Proposed Model AI Governance Framework (Model Framework) – an accountability-based framework to help chart the language and frame the discussions around harnessing AI in a responsible way. We can several organisations coming forward on AI governance. As examples, NEC released the “NEC Group AI and Human Rights Principles“, Google has created AI rules and objectives, and the Partnership on AI was established to study and plan best practices on AI technologies.

 

What could be the real-world challenges around the ethical use of AI?

Progress in the adoption of AI has shown some incredible cases benefitting various industries – commerce, transportation, healthcare, agriculture, education – and offering efficiency and savings. However, AI developments are also anticipated to disrupt several legal frameworks owing to the concerns of AI implementation in high-risk areas. The challenge today is that several AI applications have been used by consumers or organisations only for them to later realise that the project was not ethically fit. An example is the development of a fully autonomous AI-controlled weapon system which is drawing criticism from various nations across the globe and the UN itself.

“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 Lab that 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” says William.

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.

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Singapore Government Promoting Tech Adoption in the Legal Industry

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Singapore is encouraging the adoption of technology in the legal sector for higher efficiencies. In May, the Ministry of Law (MinLaw), Enterprise Singapore, the Infocomm Media Development Authority (IMDA) and the Law Society of Singapore (LawSoc) announced the launch of a new SmartLaw Guild to encourage law firms to adopt technology.

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 sector given 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.

 

Why have Law Firms been Slow in Tech Uptake?

A LawSoc survey held in 2018 showed that the adoption of technology helps in the delivery of legal services but only an estimated 12% of law firms in Singapore appears to have adopted digital technology till date. Hence, to encourage digitalisation of the legal industry, legal firms in Singapore will benefit from the SGD 3.68 million fund that has been set aside, to provide them with funding support for adopting technology solutions.

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 capabilities and 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?

Amit Gupta, CEO, Ecosystm interviewed Matt Pollins, Partner of renowned law firm CMS where they discussed the legal implications of AI as well as the uptake of new technologies in the legal industry.

What do you think are the implications of technology adoption in the legal industry?

Let us know in your comments below.

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VendorSphere: NEC’s Facial Recognition Capabilities

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I was invited recently by NEC to attend their briefing where Walter Lee, their Evangelist and Government Relations Leader presented to analysts and journalists about how they are winning large contracts across various sectors in the areas of biometrics and surveillance. Biometrics is not just used as a way to drive greater security, but is also helping increase speed in processing times, reducing waiting period in queues and used as a way to drive efficiency and reduce costs which was highlighted by Lee through the various projects NEC had won recently.

NEC’s Artificial intelligence (AI) engine, NeoFace’s strength lies in its tolerance of poor-quality images. The NeoFace solution can match images with low resolutions down to 24 pixels between the eyes and this has allowed it to demonstrate the matching accuracy which is hard to achieve for most vendors offering Facial Recognition solutions. It is its ability to work across various challenges around low resolution, light and images that has allowed NEC to be one of the leading suppliers of Facial Recognition solutions globally.

Key Case Studies Presented

In 2018 Delta Airlines launched the first ‘biometric terminal’ in the US at the international terminal in Atlanta’s Hartsfield-Jackson airport. The biometric push according to Lee replaces tickets and customers now check in by using their face. The system recognises their face and they are checked in. Customers no longer need to use their passports to get through checkpoints around the airport.  Lee emphasised on how it takes 9 minutes to board an international flight. Apart from driving identification and security, this use case highlights how airports around the world can increase efficiency in their overall check in and boarding processes at airports. Other core benefits derived from this implementation include better security for border control, seamless service, speed of boarding (savings of 9 minutes per flight). Privacy issues were addressed with regards to where the data was residing and how long the data would be kept for and in this case the data was kept for only 24 hours.

According to the global Ecosystm AI study of current and planned Facial Recognition adoption by industry, the transportation industry is leading the number of deployments globally.

Adoption of Facial Recognition by Industries

Another case study presented is the upcoming 2020 Summer Olympic and Paralympic Games in Tokyo., for which NEC will provide the Facial Recognition solution. The solution will be used to identify over 300,000 people at the games including athletes and officials. It is the first time that Facial Recognition technology will be used for this purpose at an Olympic Games. The NEC solution will allow the matching of tens of thousands of faces in a nano second according to  Lee.

The Tokyo 2020 implementation will involve linking photo data with an IC card to be carried by accredited people. NEC says that it has the world’s leading face recognition tech based on benchmark tests from the US’s National Institute of Standards and Technology (NIST).

Ecosystm Comment.

NEC has years of experience in biometrics and Facial Recognition. Not many vendors have solutions that can capture vast amounts of images in a nano second. Their solutions are used by some of the largest organisations in the world. NEC has also perfected the art of handling low resolution images which if not analysed accurately can lead to unintended consequences. The ability to process low resolution images with speed and accuracy is not something that is easily achievable. Security and the rise of terrorism are some of the needs as to why Facial Recognition is important. Additionally, speed and efficiency in administrating passenger boarding at airports whilst ensuring that the security and identity checks have been made is important. The Delta Airlines case study is a great example of how there can be a savings of 9 minutes per flight. NEC continues to gain traction in the market and the Ecosystm AI study has them as one of the top vendors being evaluated for planned implementations for Facial Recognition globally.

The benefits of Facial Recognition solutions are huge – however there must be greater scrutiny around the possible outcomes of AI. Whilst regulation on AI is still at its infancy, 2019 and 2020 will see greater scrutiny and regulation around AI implementations. These will be directed towards protecting individual’s data but also there will be greater emphasis on addressing issues around privacy, ethics and bias in AI implementations. Feeding the machine with the right data (unbiased and ethical) and measuring the various outcomes before the project goes live must be looked at with greater diligence.

2 weeks ago, San Francisco became the first US city to ban the use of Facial Recognition technology by the police and local government agencies. One of the reasons for the ban was with regard to bias. When designing the systems, if technology specialists feed the wrong information for example recognising only a certain skin colour, then the problem of making the wrong and unwanted assumptions start arising. The ecosystem of players in the AI industry ranging from government, academia right down to vendors have a greater role to play in ensuring ethics and bias issues are addressed from the onset of the project. There are consultants in the market as I highlighted in my recent Ecosystm report, that prepare companies for the impact of ethics, fairness and bias. We can expect more of such consultancies and specialist agencies to grow in the market.

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.

 

6
How IoT and AI will transform the sports business?

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This is an abstract of my presentation in Dubai on 23rd April 2019. I want to convey my special thanks to Dr. Eesa Bastaki, President of the University of Dubai for inviting me on the occasion. It was a magnificent experience delivering at such a great University.

In the year 2016, I considered Rio as the first Internet of Things (IoT) Olympic games in my article “The future of “The Internet of Olympic 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?

Athletes

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

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.

Judges

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.

Fans

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.

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Automation Versus AI – Building the Business Case for 70% Accuracy

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I ran several roundtables over the past few weeks speaking to business and technology leaders about their AI investments – and one factor came up many times – that it is hard to build a business case for AI because 70% accuracy was not good enough…

What this means is that companies have thousands of things to automate. Most of those automations in the short-medium term will deliver 100% accuracy using RPA and other simple automation tools. Every time you run that process you know the outcome.

Along Comes AI and Machine Learning

These dumb processes can now learn – they can be smart. But originally they won’t deliver 100% accuracy. They might only deliver 60-70% to start with – climbing perhaps to 90%. The benefits of these smart, learning processes can amaze – costs can fall, processes can improve, outcomes can accelerate. But traditionally we have built technology business cases delivering 100% accuracy and outcomes.

So we need a new way to think about AI and a different language to use about the way it works. The people who sign off on the business cases might not understand AI – they will come to the business case with the same lens they use for all technology investments (and evidently – all business investments). We also need to be better at selling the benefits to our leaders. CEOs and Managing Directors in the roundtables are surprised to hear that AI won’t deliver 100% accuracy – they said unless they know more about the capability, savings and outcomes that the solution might drive, they are unlikely to fund it.

Make Your Dumb Processes Smart

I take this as good news. It means we have moved beyond the hype of AI – the need to “do AI in our business” that drove many of the poorer chatbots and machine learning projects. It means that businesses review AI investments in the same way as any business investment. But it also means we can’t over-promise or under-deliver on AI. Woodside did this with their initial foray into AI, and they are still playing catch up today.

While there are many opportunities to use “dumb automation” and save money, reduce or redeploy headcount – or have employees focus on higher value activities or make real differences to customer experiences – there are as many opportunities to make dumb processes smart. Being able to automatically read PDF or paper-based invoices – processes usually done by humans – could be a huge saving for your business. OK – maybe you can’t redeploy 100% of the staff, but 70% is still a big saving. Being able to take human error out of processes will often help to save money at two steps on the process – automating the human input function up front and also getting rid of the need to fix the mistake.

Start Your AI Journey With The Low Hanging Fruit

Ecosystm’s Global Ongoing AI study has shown that most businesses are focusing their AI investments on internal initiatives – on reducing process time, cost savings and driving productivity – which makes the most sense today. They are the easier business cases to build and the easiest benefits to explain.

 

Perhaps AI is also a chance for businesses to acknowledge that “efficient” does not always mean “good”. Many of the processes we automated or coded to ensure 100% compliance don’t give customers or employees what they are looking for. And maybe making the customer happy 70% of the time is better than not making them happy at all…

If you’d like to dig deeper into Ecosystm’s reports exploring the data from our ongoing AI study – check them out here (you’ll need to register if you have not already – it is free to register, but some content is premium):

4 Vendors Emerge as Leaders: Understanding the AI Vendor landscape

Use Cases Drive AI Software Adoption: Understanding The Industry Landscape

 

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Nvidia and Intel Race For The Future Of Machine Learning

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Two things happened recently that 99% of the ICT world would normally miss. After all microprocessor and chip interconnect technology is quite the geek area where we generally don’t venture into. So why would I want to bring this to your attention?

We are excited about the innovation that analytics, machine learning (ML) and all things real time processing will bring to our lives and the way we run our business. The data center, be it on an enterprise premise or truly on a cloud service provider’s infrastructure is being pressured to provide compute, memory, input/output (I/O) and storage requirements to take advantage of the hardware engineers would call ‘accelerators’. In its most simple form, an accelerator microprocessor does the specialty work for ML and analytics algorithms while the main microprocessor is trying to hold everything else together to ensure that all of the silicon parts are in sync. If we have a ML accelerator that is too fast with its answers, it will sit and wait for everyone else as its outcomes squeezed down a narrow, slow pipe or interconnect – in other words, the servers that are in the data center are not optimized for these workloads. The connection between the accelerators and the main components becomes the slowest and weakest link…. So now back to the news of the day.

A new high speed CPU-to-device interconnect standard, the Common Express Link (CXL) 1.0 was announced by Intel and a consortium of leading technology companies (Huawei and Cisco in the network infrastructure space, HPE and Dell EMC in the server hardware market, and Alibaba, Facebook, Google and Microsoft for the cloud services provider markets). CXL joins a crowded field of other standards already in the server link market including CAPI, NVLINK, GEN-Z and CCIX. CXL is being positioned to improve the performance of the links between FPGA and GPUs, the most common accelerators to be involved in ML-like workloads.

Of course there were some names that were absent from the launch – Arm, AMD, Nvidia, IBM, Amazon and Baidu. Each of them are members of the other standards bodies and probably are playing the waiting game.

Now let’s pause for a moment and look at the other announcement that happened at the same time. Nvidia and Mellanox announced that the two companies had reached a definitive agreement under which Nvidia will acquire Mellanox for $6.9 billion.  Nvidia puts the acquisition reasons as “The data and compute intensity of modern workloads in AI, scientific computing and data analytics is growing exponentially and has put enormous performance demands on hyperscale and enterprise datacenters. While computing demand is surging, CPU performance advances are slowing as Moore’s law has ended. This has led to the adoption of accelerated computing with Nvidia GPUs and Mellanox’s intelligent networking solutions.”

So to me it seems that despite Intel working on CXL for four years, it looks like they might have been outbid by Nvidia for Mellanox. Mellanox has been around for 20 years and was the major supplier of Infiniband, a high speed interconnect that is common in high performance workloads and very well accepted by the HPC industry. (Note: Intel was also one of the founders of the Infiniband Trade Association, IBTA, before they opted to refocus on the PCI bus). With the growing need for fast links between the accelerators and the microprocessors, it would seem like Mellanox persistence had paid off and now has the market coming to it. One can’t help but think that as soon as Intel knew that Nvidia was getting Mellanox, it pushed forward with the CXL announcement – rumors that have had no response from any of the parties.

Advice for Tech Suppliers:

The two announcements are great for any vendor who is entering the AI, intense computing world using graphics and floating point arithmetic functions. We know that more digital-oriented solutions are asking for analytics based outcomes so there will be a growing demand for broader commoditized server platforms to support them. Tech suppliers should avoid backing or picking one of either the CXL or Infiniband at the moment until we see how the CXL standard evolves and how nVidia integrates Mellanox.

Advice for Tech Users:

These two announcements reflect innovation that is generally so far away from the end user, that it can go unnoticed. However, think about how USB (Universal Serial Bus) has changed the way we connect devices to our laptops, servers and other mobile devices. The same will true for this connection as more and more data is both read and outcomes generated by the ‘accelerators’ for the way we drive our cars, digitize our factories, run our hospitals, and search the Internet. Innovation in this space just got a shot in the arm from these two announcements.

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