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.
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):
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.
I recently published my predictions on AI (seeblog here and download or view the more comprehensive report here). The prediction that got the most feedback and discussion concerns the likelihood of a large acquisition or merger based on AI assets. With AWS, Microsoft, Google and IBM dominating Ecosystm’s list of current and future preferred AI suppliers in our AI study, other companies (such as SAP, Oracle, SAS and Salesforce) want to be the choice for AI platform. As we published our AI predictions, this one was already coming true (to an extent anyway!) with SAP’s acquisition of Qualtrics.
But the question has been asked “why is AI so important”. And my answer to that question is “because, for software companies, AI is the end game…”
What do I mean by that? Well we are not too far away from a day where traditional enterprise applications are no longer relevant. ERP, CRM, HRM, SCM etc will all disappear and be replaced by an AI engine. The purpose of those traditional systems was to simplify, codify, and automate business and customer processes. ERP, CRM, and the rest are already starting to use algorithms to drive semi-custom (typically pre-coded) business processes. But in the mid-term future, we will have a time where the entire process is intelligent – where the system/application creates the best business process for the customer on the fly. I’ll take you through an example:
A customer comes to your website – the site will look at the information it has on the customer (either as a registered customer or a non-registered one, where it will scour cookies, IP addresses, location, social information – Facebook, Pinterest, Google etc) and then will create an experience designed for that customer – e.g. it might know the customer is based in New York City, is a Mets and Rangers fan, viewed posts on Facebook about global warming, is female, 42 years old, has kids etc. It uses the language of the customer, words that they would relate to, and the level of detail they would expect. It puts the products or services forward that best match that customer’s potential needs.
The customer orders 10 identical products as gifts for friends for Christmas – but the provider does not have a location with any more than 4 of those products – so the intelligent system sources the 10 from different locations and organises multiple shipments. One of the locations can’t ship until after xmas – but the intelligent system decides that the customer is important so puts in a request for Uber to pick up from that location and do the delivery. However, there is no automatic integration with Uber – so the intelligent system creates a real time custom integration with Uber.
The customer also asks the question if they can pay with AliPay – which the supplier does not accept as standard – however again the intelligent system creates a real time integration with AliPay in order to complete the transaction. The customer gets the goods they want – quickly – and gets to pay in the way they want. The system accounts for the revenue and moves it to the right bank accounts, co-ordinates follow-up orders with suppliers, and adds the sales information to the real-time sales analytics. It also crafts a unique email welcoming the customer and adds them to the customer database.
The intelligent system created a unique process in real-time as it interacted with the customer using text, images, video and voice. The system understands what your business is trying to achieve and what the rules are.
Such a capability is not that far away – and it makes existing enterprise applications and integration platforms redundant. THIS is why AI is the end game – if you aren’t the chosen AI platform in your customers, you might not be in your customers plans for much longer.
In most organisations that transition will be slow – applications will get smarter, and will move from standardised processes to unique processes slowly. These organisations will start from their application investments and work outwards from there. But other companies will start from their cloud-based AI platforms and partners – and reinvent their businesses in the cloud on these platforms. Others will do both – and at some stage in the future need to decide on which AI platform they standardise on…
Therefore mergers and acquisitions in the AI market are inevitable. Applications, cloud and analytics providers will build and buy capabilities, customers and market share in order to position themselves as the key AI platform in their clients. For many technology vendors, the next few years will be integral to their long term success. AI will change the technology provider landscape as we know it today – get strapped in for a fun ride!
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Machine Learning and IoT Sensor Analytics Will Drive AI Growth In 2019
The Global Ecosystm AI Study shows that the growth in AI over the next 12 months will come from Machine Learning (ML), as this capability is applied to a plethora of problems and challenges across the business. IoT Sensor Analytics will also see strong growth – due to the growth in IoT implementations and subsequent exponential growth of the data coming off these sensors and the desire to do something intelligent/different with this data.
The Growth in IoT Will Fuel the Growth in AI
Today, many organisations are deploying IoT solutions. These sensors are already creating – and will continue to create large amounts of data. While these sensors today are, for the most part, one-way (i.e. collect and analyse data), we are getting closer to the point where many of these sensors will be bi-directional (i.e. sense and respond). Businesses will look to AI tools – particularly IoT Sensor Analytics and ML – to help them learn from that data and respond accordingly. In many ways the future success of IoT and AI are interdependent.
In the Short Term, AI Will Create More Jobs than it Removes
Much of the media focus on AI has been around the jobs that will disappear in economies driven by AI and the automation that it will enable. But in 2019 (and over the next few years), AI will create more jobs than it removes. How is this? Firstly, we are seeing AI do a lot of jobs that are not even done today – analysing images for trends that humans did not see, looking for correlations in data sets that we did not know existed. Secondly, even where automation and AI are driving productivity, the vast majority of organisations are taking the opportunity to reskill those people. AI-driven profit will be ploughed back into businesses and create more employment opportunities – some of which we can imagine today and some we cannot. Thirdly, there is the vast hiring that organisations have started to undertake to bring on board the skills they will need to make their business smarter with AI. Many of these jobs today are in addition to, not replacing existing resources.
Bimodal IT Departments Will Slow Down AI Implementations
Many of the digital capabilities that businesses have been building over the past five or so years have not required active participation by the IT team. What started as “shadow IT” initiatives became the standard way to deliver customer and business value as smart organisations pushed their technology resources into the product and customer teams, so they could drive innovation at pace. But AI initiatives involve training algorithms with data – the more data the better the algorithms. Business leaders will need to work with IT to get access to this data – that typically resides in “back-end” systems – to train their models. At this step, many bimodal IT departments will kick the project into slow mode, because the data sits in “slow mode” back-end systems. The project will be managed with “slow mode” processes, using heavy-handed governance and processes to turn what could have been a six-week project into a six month one.
A Merger of Massive Scale Will be Driven by AI Assets
According to the Global Ecosystm AI Study, Microsoft, IBM, AWS and Google account for 62% of current and planned AI implementations – and that dominance is set to continue for the foreseeable future. This means a lot of other big companies miss out. SAP, Oracle and Salesforce are hoping that AI will help them get deeper within their existing customers and also expand beyond their current client base. Therefore, we expect a massive merger (in USD billions) driven by the AI customers and assets of the technology vendor. Technology companies that are used to dominating their industries – Cisco, HPE, Dell EMC, SAS and others could be left behind if they do not get scale quickly in the AI space – so a major merger is on the cards.