Monthly Archives: October 2018

AI-as-a-Service: All That You Need To Know

Hussain Fakhruddin
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Hussain Fakhruddin

Hussain Fakhruddin is the founder/CEO of Teknowledge mobile apps company. He heads a large team of app developers, and has overseen the creation of nearly 600 applications. Apart from app development, his interests include reading, traveling and online blogging.
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AI services: Overview

In 2008, the worldwide software-as-a-service market was worth only $5.6 billion. Cut to 2020, and that figure is expected to soar to $133 billion – clearly indicating the rapid rise in demand for consumption-based software services (‘a la carte software’, so to speak). Between 2018 and 2020, the total number of SaaS subscriptions are set to jump by nearly 96%. This is, without a shadow of a doubt, one of the fastest growing technology sub-domains at present.

While services like Platform-as-a-Service (PaaS) and Infrastructure-as-a-Service (IaaS) have been in discussion for some time now – the ‘as-a-service’ market is gradually being extended into newer, more cutting-edge, fields. The artificial intelligence-as-a-service (AIaaS) market is a classic example of that. According to estimates, the worldwide AIaaS market will be valued at just a shade under $11 billion by the end of 2023, with the 2017-2023 CAGR hovering around the 49% mark. The biggest of players, like Microsoft, Google, IBM and Amazon, are already heavily active in this field. In today’s discussion, we will take a look at some interesting facets of the growth of AIaaS:

  1. What exactly is AIaaS?

    As the name itself suggests, AIaaS refers to off-the-shelf artificial intelligence service offerings that can be bought and implemented immediately. In other words, it can be explained as ‘third party AI service offerings’ as well. Like all other _ -as-a-Service packages, AIaaS also makes use of cloud computing – and can add significant strategic flexibility to the operations of organisations, pulling up efficiency and productivity levels. Since AIaaS solutions are typically dynamic and highly adaptable, they also help in optimising the effectiveness of big data analytics. With these ‘readymade’ AI services, it becomes possible for companies to derive all the key advantages of artificial intelligence – without actually having to make huge investments (and bear the associated risks) for building their very own cloud platforms. The onus, however, lies with company CEOs and IT specialists to understand the precise type of AI service they require, and the potential benefits. AIaaS has multifarious benefits – but it should not be adopted without adequate initial research.

Note: While the popularity of AIaaS is a fairly recent trend, the concept of ‘artificial intelligence’ is far from being a new one. At present, we have vendors that offer multifunctional digital platforms powered by machine learning (apart from general cloud AI service providers).

  1. Will AIaaS emerge as a worthy substitute of human intelligence?

    The comparison is an erroneous one to begin with. Contrary to what many think (and indeed, what the concept of AI has meant for years), artificial intelligence is not ONLY about replicating the capabilities and (probably) the cognitive prowess of human beings. Instead, AI should be viewed as an end-to-end technology – which uses various techniques and modules to analyse data better, identify patterns and trends, and calculate the probabilities of different end results (say, for predictive purposes). Broadly speaking, two different types of algorithms – the deep learning (DL) algorithms and the machine learning (ML) algorithms – are used in full-fledged AIaaS services. The prime objective for implementing AI solutions is to enhance the capabilities of existing IT setups, and allow them to ‘learn’ new functionalities (without additional coding having to be done). The entire artificial intelligence vs human intelligence debate is overhyped, and in most instances, misplaced. The two should ideally complement each other.

Note: The need to collect and securely store big data is going up rapidly for companies. AIaaS makes artificial intelligence tools more accessible – and hence, help a lot in data handling/management requirements.

  1. What are the main types of AIaaS?

    For AI to indeed deliver the desired results, enterprises have to select and correctly deploy the ‘right’ type of AIaaS first. Doing so, in turn, requires the IT managers to be aware of the different types of these ‘ready-to-use’ AI services. Broadly, there are 4 different forms of AIaaS: first, there are the customised machine learning (ML) platforms and frameworks, that can create data models and and can ‘read’ patterns from existing data pools. Next up, there are the AI-powered bots – powered by the ever-improving natural language processing, or NLP, capabilities (in fact, chatbots are the most popular use cases of AIaaS). Then, we have the entirely managed ML services – which make use of drag-and-drop tools, cognitive analytics and custom-created data models to generate more values (compared to the general machine learning frameworks). The fourth type of AIaaS includes the third-party APIs (application programming interfaces) – which are built to add extra functionalities to any new/existing application. All that organisations willing to join the digital transformation revolution have to do is identify the type(s) of AIaaS that are likely to boost ROI figures, purchase them from AI vendors, and start implementing them immediately. Small changes, if required, can also be made.

Note: Apart from Microsoft, Amazon and Google, several other companies – like SalesForce and Oracle – are also highly active in the AIaaS space.

  1. How fast is the AIaaS market growing?

    As competition rates are increasing and digital technology is getting more and more refined, the AI-as-a-Service sector is growing rapidly (~$11 billion in 2023). From a $4810 million valuation last year, the global market for artificial intelligence will jump to well over $88500 million by the end of 2025. The growing demand among organisations for using cutting-edge machine learning services on the cloud is also pulling up investment figures. A recent report estimated that overall expenses on AI will show a 4X increase between 2017 and 2021 – as different industries start to adopt AIaaS solutions. The biggest advantage of AIaaS is it allows enterprises and workers to focus on their core capabilities/lines of business – without having to worry about model building or cloud network development. Over the next half a decade or so, the growth of AIaaS will further gather momentum – and developers will be increasingly incorporating AI capabilities in both applications and big data systems.

Note: An enterprise-level study found that 8 out of every 10 companies prefer using multi-cloud models. Among them, specialised hybrid cloud services are the most in demand.

  1. Does the AIaaS market have different segments?

    The scope of artificial intelligence in general, and AIaaS in particular, is huge. As such, trying to understand everything about the service at one go can be complicated, and in fact, an exercise in futility. For purposes of research clarity – the AIaaS domain is divided in different segments, based on different parameters. According to functionality, there are the ‘managed services’ and the ‘professional services’, while from the technology perspective, we have the DL and ML services on one hand, and high-end NLP capabilities on the other. AIaaS can also be segmented in terms of the software tool(s) that lies at the heart of it – web/cloud APIs, processor tools, data archiving and storage, and others. In terms of usability, AIaaS is finding rapid adoption in different industry verticals – right from retail services, transportation, and banking & finance, to healthcare, manufacturing and telecom services (the impact of AI services on the public sector is also going up gradually). A wide range of customisations are also available, enhancing the usability factor of AIaaS.

Note: In the transportation sector, AI-as-a-Service can be used to make tasks like navigation, finding the fastest routes, and parking, simpler than ever before.

  1. What advantages does AIaaS deliver?

    The benefits of deploying AIaaS have a lot in common with the general advantages of any consumption-based (i.e., on-demand) software service. For starters, the seamless scalability is a big factor – since this allows enterprises to start off small, and then increase the scale of AI operations over time (according to project-specific requirements). In a scenario where the need for super-fast graphical user interfaces (GPUs) and parallel machines is going through the roof, AIaaS comes in handy – since it makes it possible for IT managers to implement and use the latest AI-powered infrastructure, without having to be concerned about the lofty expenses. Since AI-as-a-Service is, by definition, ready to use – the challenges posed by the relatively complicated nature of traditional AI solutions are bypassed. Yet another factor in favour of these off-the-shelf AI services is the complete transparency. Users have to pay only to to the extent of their use of the services – instead of arbitrary amounts and high overheads. Smarter AI-powered operations at easily manageable budgets – that’s the key for AIaaS for delivering value to enterprises.

Note: Machine learning plays a mighty important role in facilitating ‘intelligent optimisation’ for different industries.

  1. What factors are driving up the demand for AIaaS?

    Ours is a data-driven environment, and in here, the value of real-time decision-making capabilities can hardly be overemphasised. This, in turn, serves as a key driver of AIaaS solutions. The volume of data obtained from specialised, smart sensors, UAVs and different types of IoT applications is expanding exponentially – and the need of the hour is for improved, intelligent data management, use, accessibility and security. AIaaS is ideal for smarter big data management, as well as for helping computing systems perform specific tasks (with the help of ML modules). Since these services are available as ready-to-use packages from vendors, the development/deployment time is minimised. The fact that AIaaS can be used by practically everyone (thanks to the user-friendly underlying algorithms) also boosts its demand. The growing need for faster GUIs, and customised APIs also acts as an important driver for this market. For cloud providers in particular, and for businesses in general, AIaaS can deliver significant competitive advantages.

Note: The Distributed Machine Learning Toolkit by Microsoft allows users to run multiple ML applications simultaneously. Predictive analytics, speech recognition and translation services are included in the Google Cloud Platform. IBM has its very own Watson Developer Cloud.

  1. Will growth of AIaaS increase the demand for specialist data scientists?

    Yes, and in a big way. What’s more – as AIaaS starts to become mainstream, more time and higher budgets will also need to be allocated. Given the heavy investments (maybe not at the start, but certainly in the long-run) involved and the potential benefits, it is only natural that companies will ramp up their search for IT professionals with high expertise and a lot of relevant experience. These data scientists will be responsible for working with different types of customised AI algorithms. Over the years, AI solutions have mostly been used by the largest players – simply because others did not have qualified, adequately trained manpower (and tech generalists were not enough). However, with the proliferation of AIaaS, a new generation of AI data scientists will appear – and companies of all sizes will be able to hire them and take advantage of artificial intelligence/machine learning. Make no mistake – AI is a complex technology, and proper qualified personnel are required to handle it.

Note: Amazon Web Services is still the market leader in the public cloud domain. However, Microsoft Azure is growing the fastest in this sector. Google Cloud and IBM Cloud occupy the third and fourth spots respectively.

  1. Are there any challenges/barriers for AIaaS?

    For all its advantages and relative ease of use, there are certain points of concern about AIaaS (like any other new tech service!). Since users have to depend on third-party AI services for the data/results/information required, unforeseen delays can crop up. The greater reliance on external service providers can also pose data security challenges – since quite a lot of business-critical data have to be shared with the third-party vendors. The key here is to ensure that the chosen AIaaS has robust security and data governance standards, to rule out unauthorised access. Once we go beyond the initial cost-advantages of AIaaS (over traditional AI), the chances of expenses going up in the long-run – as the technology gets more refined and more complex – also become apparent. Since the vendors provide AIaaS as a package offering, it is impossible to really understand the internal AI mechanisms – although the data inputs and the expected results are known. As a result, the overall transparency of the AI services gets reduced. Over the next few quarters, the technology will get more advanced, and we can reasonably expect that most of these challenges will be satisfactorily resolved.

Note: Serverless technology is leading the way in cloud service adoption. Container-as-a-service (CaaS) is also fairly popular.

     10. How important is it to select the right AIaaS for business?

Let’s just put it this way: if a AI service is implemented without adequate background research, the entire thing can turn counterproductive. At the very outset, a company has to take a stand on whether it at all needs AIaaS solution(s). A thorough comparison between AIaaS platforms and self-coded implementations also needs to be done – to get a fair idea on which option will be more suitable. Users also need to continuously test the AI services, to make sure that they are performing at optimal levels. In any AIaaS, the process of implementing the algorithms is not explained – and that makes thorough AI testing all the more important. In ‘low-level APIs’, there can be glitches in the process pipeline – which need to be identified and removed quickly. As already highlighted above, awareness of the different types of AIaaS, and their respective functions and utilities, is also an absolute must. AIaaS is a vital cog in the digital transformation journey of enterprises – but only if it is chosen and implemented correctly.

Note: According to a research report, nearly 36% of all the expenses on cloud services are wasted. Going forward, the focus has to be on reducing this figure.

      11. How about the importance of AIaaS in the public cloud?

A 2018 RightScale report found that, 67% users are set to increase their spendings on cloud services by at least 20% (18% companies have plans to double their cloud expenses). The adoption of AI-as-a-Service is rising across the board in the public cloud – with both AI data practices as well as AI computing capabilities developing continuously. The recent advancements in neural networks and deep learning mechanisms are also instrumental in pulling up the adoption of AIaaS in the public cloud space. Cloud vendor companies are offering ready-to-use APIs which do not require elaborate machine learning models – enhancing the convenience factor. In the public cloud, AI services can broadly be classified under three heads: cognitive computing, conversational artificial intelligence, and custom cognitive computing. The AI data infrastructure, on the other hand, includes RDBMS, Data Lake and NoSQL.

Note: Cutting down on total expenses is the biggest point of concern for cloud users as present. Generating better financial reports and porting more workloads on cloud are also things that are being focused on.

       12. AIaaS: The future

In terms of adoption and market share, North America (with a 46% share) is the clear leader in the global AIaaS sector. Europe, with ~28% share, occupies the second position, followed by the Asia-Pacific. There is also a definite ‘gap’ in how the services are being used – since only around 33% of the ‘AI companies’ actually leverage artificial intelligence in any meaningful way. In the next couple of years, more users will ‘understand’ the potentials of AIaaS and the far-reaching scopes of the technology – and the deployments will be more effective. The market for web APIs and cloud APIs is set to witness healthy growth, while the NLP market is also on an upward spiral ($21+ billion by 2025). The markets will continue to grow, and as the technology becomes more nuanced – we are sure to see more interesting use cases for specialised AI services.

More than 60% professional marketing experts feel that artificial intelligence is the most important element in their overall digital strategies. AIaaS makes the technology easily accessible – with users being able to enjoy the benefits at a much lower cost. Of course, to truly generate value and improve ROI figures, AIaaS has to be used smartly (with in-depth research). According to reasonable estimates, AI services can push up productivity by up to 40%.

The AIaaS market will continue to grow stronger in the foreseeable future. It remains to be seen how companies manage to use it as a key differentiator, and stay ahead of the competition.

 

Top 12 Trends In Digital Transformation To Watch Out For In 2019

Hussain Fakhruddin
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Hussain Fakhruddin

Hussain Fakhruddin is the founder/CEO of Teknowledge mobile apps company. He heads a large team of app developers, and has overseen the creation of nearly 600 applications. Apart from app development, his interests include reading, traveling and online blogging.
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Digital transformation trends

 

In the United States, the total revenues from the digital transformation market will go beyond $1.1 trillion by the end of this year. Moving on to the Middle East – experts estimate that digital solutions will augment annual GDP figures by well over $93 billion. The world of technology is evolving constantly – and things that were, even half a decade (or less!) ago possible only in the realms of fantasy, are actually being deployed in the real world (think about roads with ONLY driverless cars, and you will get the picture). The internet of things (IoT) will continue to be the face of this digital revolution, with cloud technologies, artificial intelligence, and AR/VR all becoming smarter, more usable, and more targeted towards addressing real-life problems.

For all the focus and discussion about digital transformation worldwide, there still remains a definite gap between awareness and implementation in this domain. While, on average, 9 out of every 10 entrepreneurs believe that full-blown digital strategies can deliver competitive advantages to their businesses – <20% actually have proper plans and funding schemes in place (less than 10% enterprises are ‘fully digital’). From blockchain and edge computing, to IoT, big data, analytics, AI chatbots and stronger digital integrations – there are lots of avenues for the digital economy to grow further over the next few quarters. In what follows, the most important digital transformation trends for 2019 have been listed:

  1. AR applications to soar

    For all its exciting features and sheer ‘newness’ of virtual reality (VR) – the fact remains that VR has limited usability in the real-world, apart from high-end gaming. In 2019 and beyond, it is going to be augmented reality (AR) that will drive the digital world forward. Already, the demand for specialised enterprise AR applications (say, for training) is on the high – and going forward, this technology will further gather momentum. The key here is that AR has both the innovativeness AND the practical usability (unlike VR, where the usefulness is rather restricted). As the technology gets more and more refined, we are likely to witness a surge in the number of use cases for AR. New augmented reality and mixed reality (MR)-powered products/kits are set to be launched over the next few quarters, and there will be an increased need for experienced AR developers.

Note: Mobile AR is going to lead the way, even as the market for AR headsets also continues to grow steadily. Extended Reality (XR) – a mix of VR, AR and MR – will be the newest tech buzzword.

  1. Focus on delivering improved digital experiences to users

    Digital transformation is, or at least should be, all about providing seamless end-user experience to everyone. That, and that only, can bolster customer satisfaction and customer-retention rates. An improved digital ecosystem also needs to include better workplace experiences for employees, partners, and other stakeholders. In the next few quarters, many organisations will start investing big on new tools, digital methodologies and overall infrastructure – in a bid to make their enterprise IT architecture more robust. There is still a major gap in the scalability of digital experiences delivered by many companies – and as the importance of technology and tech transformation for business acceleration is realised, organisations will look to plug this gap. Investments on digital activities for enterprises will go up fairly rapidly in 2019.

Note: AI-powered ‘intelligent assistants’ and IoT will boost the customer-experience factor (CX) for the new-age, ‘smarter’ buyers.

  1. Digital transformation from the top down, finally

    It is almost impossible to attain digital maturity without expert management. While the different ‘C-suite personnel’ (CIOs and COOs and the like) generally handle the digital ecosystems and IT infrastructures in a company – 2019 will probably be the year when the CEOs start taking greater responsibility for the digital future of their respective organisations. Apart from being more effective, this move will be in line with the preferences of general employees – who typically like the digital directions to come from the top-level. As the CEOs start taking greater control, the ‘C-suite’ officers will gradually move to the background. More importantly, the myth that digital initiatives are something that only the IT or the marketing departments need to be concerned with with will be busted. The onus will be squarely on the entrepreneurs/CEOs to make the right recruitments (for greater business agility, specialisation and digital transformation). The importance of acquiring more reliable data, and upgrading the skillsets of existing employees, also has to be identified.

Note: On a YoY basis, the role of CEOs as the face of digital transformation has jumped from 22% to ~40% in 2018. The importance of CIOs, on the other hand, has gone down to 16% (from 24% last year).

  1. Chatbots to find greater acceptance

    The performance of AI-chatbots for business has come under scrutiny several times over the last few quarters. What’s more, there is also a cloud of uncertainty over how widespread adoption of chatbots will affect employment (i.e., whether there will be major job-displacements). Even so, the growth and improvement of chatbots will continue to be one of the strongest digital transformation trends for 2019 – with 4 out of every 10 big companies adopting chatbots before the end of the year. The ongoing advancements in ‘sentiment analysis’ and ‘natural language processing’ (NLP) are making AI-chatbots smarter and more reliable than ever – ensuring that users can get access to deeper customer insights, and provide customised services accordingly. As far as the effect on human workforce is concerned, companies will realise that chatbots need human touch and guidance for optimal functionality (handing over a buyer query to a human executive, for example). Chatbots are NOT meant to be substitutes of human workers – and provided that the necessary upskilling/training takes place – large-scale job losses should not occur.

Note: By the turn of the decade, 25% of all customer service activities will be handled by AI chatbots (also known as ‘virtual customer assistants’).

  1. Move towards more integrated digital endeavours

    Digital transformation, in the truest sense of the phrase, requires considerable investments and retooling. The fact that more than 70% of all transformation initiatives end up in failure is alarming – and one of the biggest reasons for the common failures is the adoption of a half-hearted, fragmented approach. In the next year and beyond, we will see digital initiatives becoming more integrated – and traditional organisational silos being broken down (the adoption of DevOps culture – bringing together the ‘development’ and ‘operations’ departments – is going to play a vital role in this). The benefits of following an integrated approach for digital transformation are immense – ranging right from better planning and development of powerful data models, to more consistent business growth and greater economies of scale. Top-level digital integrations have emerged as serious business imperatives, and in 2019, many companies will launch their very own digital programs and projects.

Note: Nearly 86% of all enterprise decision-makers feel that digital initiatives have to be integrated properly within the next 20-24 months. Fragmented efforts are almost certain to fail.

  1. The hype is over for blockchain technology

    That’s not to say that blockchain is a flop though. The problems here are cropping up from the lack of a single standard method of blockchain implementation. Since the user-requirements also vary widely (finance to marketing to HR, and more) – modifying the technology becomes an unduly complicated task. However, interest in blockchain is definitely rising – and leading players are examining the usability of blockchain technology for use cases outside of financial services and cryptocurrency (for example, transportation & logistics). Contrary to the hefty growth predictions, developments and experimentations with blockchain will continue right through 2019 – and researches on increasing the mass adoption of the technology (a plug-and-play blockchain model should help) will be conducted. The potential of blockchain is huge – but we will have to wait for a few more years before the technology becomes implementable on a large-scale.

Note: The role of blockchain in the internet of things (IoT) will be interesting. As per IDC reports, blockchain will be enabled in ~20% of all IoT deployments, by the end of 2019.

  1. Greater focus on digital education, training and skill development

    For nearly 42% organisations, lack of adequately trained/qualified personnel is a major problem in the road to digital transformation. More often than not, all the importance and focus is placed on the tech aspects of digital initiatives – relegating the ‘people-factor’ to the background. As a result, major skill gaps – together with business culture roadblocks, underutilisation of talents, and problems related to employee mindsets – become apparent, leading up to sub-par enterprise performance and productivity. Over the next couple of years or so, companies will continue to work towards changing their work environments AND upskilling their workforce (along with new hirings of digital specialists). People have to be made aware of the importance of digital transformation, the day-to-day workflow benefits, the procedure for digital deployments, and the changes in workplace culture involved. Greater commitment for training workers – so that they are actually ready (read: have the skills for) to handle digital transformation – is required. Tech advancements and innovations are all very nice – but it is the human workforce that is the biggest asset of any enterprise. Digital initiatives have to be ‘enabled’ by the workers – for them to deliver the desired results.

Note: On a worldwide scale, the total expenditure on digital transformation will go beyond the $3 trillion mark in 2019.

  1. 5G on mobile to become a real possibility

    Let’s face it, the discussions about the ‘lightning-fast’ 5G technology has been going on for a rather long time. Already, several big players, like Nokia, Samsung and Qualcomm, have initiated fixed 5G deployments - with varying success. However, for the average user, not much has changed – and it is hardly uncommon for mobile handsets to fall back to 3G (maybe even EDGE) speeds from time to time (location plays a big role here). 2019 can finally be the year when 5G mobile becomes mainstream across the board – in both urban and rural localities. Companies like Mimosa Networks are paving the way for fixed 5G wireless access (FWA 5G) – and the next stage is surely the arrival of 5G on smart devices (powered by Verizon, ATT, and other major network service providers). It remains to be seen how big the speed advantages of 5G on mobile actually turn out to be – and what improvements need to be made. For iPhone-users, the wait for 5G is probably going to be a few months longer. This year saw rapid progress in fixed 5G, and the next year will be the one when 5G mobile takes flight.

Note: Early adoption of 5G technology is already lifting the sales of Ericsson’s network equipments. The tussle between Samsung Galaxy X and the Huawei 5G handsets next year will also be fascinating.

  1. The rise and rise of ‘X-as-a-Service’

    Salesforce is leading the way in the ‘CRM-as-a-Service’ domain. There is considerable buzz over the ‘AI-as-a-Service’ sector – in which major advancements are expected in 2019 and 2020. We are steadily moving towards a world of consumption based IT offerings – where everything is going to be available ‘ -as-a-Service’. Apart from delivering more customised solutions, such consumption-based digital services will ensure greater flexibilities, end-to-end scalability, and efficiencies at every stage. Not surprisingly, IT experts and CIOs are on the lookout for ready-to-use ‘X-as-a-Service’ tools – to manage workloads better, and generate competitive advantages for their enterprises. In fact, IT-as-a-Service (ITaaS) has already been accepted as a key pillar of digital transformation – thanks to its manifold advantages, like access to latest tech tools & resources, more agile workflows, and considerably shorter procurement cycles. The growth of ‘X-as-a-Service’ will become even faster in the foreseeable future.

Note: There is also a definite trend of trying to reduce technical debt – in the form of fragmented and inconsistent data collections. The open enterprise microservices can come in handy over here.

       10. More effective utilisation of big data

Be it artificial intelligence or machine learning, or simpler analytics systems – the accuracy (and hence, utility) of everything hinges on the quality and availability of relevant, authentic data. While the fact that close to 89% of all available data at present has been created in the last 12-14 months – organisations, on average, manage to use <1% of the total big data available to them. In the coming year, this percentage will, hopefully, go up to 4-5% – as enterprises adopt better data processing capabilities. That, in turn, will take up the value generated by ML applications by several notches. Data is the single-most important factor for smarter decision-making – and organisations have started making a concerted effort to access and analyse data with greater precision. A lot of work remains to be done though – and we have to wait and see what breakthroughs are brought about by players like Microsoft, SAP and SalesForce. The more quickly we understand the potential of big data and start using cutting-edge digital tools for faster, better data processing tools – the faster will we be able to take AI and ML to the next level.

Note: The value of the global big data and related services market will rise to just a shade under $50 billion in 2019.

     11. More reliance on connected clouds

There is a myriad of needs for superior cloud services – right from faster and more secure networking, to cloud-source storage and seamless app deployments. In many cases, relying on only the private cloud or the public cloud spaces will not be enough – and what’s needed is a combination of public, private and data center resources. Such ‘connected cloud’ networks will continue to grow in 2019 – and this growth will depend on the precise business requirements of users. Over the last couple of years, a series of high-profile acquisitions (CloudHealth by VMware, Cloud Technology Partners by HPE) have clearly indicated the rising interest for highly secure ‘connected clouds’. Companies like Alibaba, Google and Amazon are also active in this domain. In the near future, standalone public or private clouds will gradually give way to ‘multiclouds’ – with the latter offering completely streamlined experiences to users. A mix of cloud-based workloads is what we can look forward to in 2019.

Note: Easier system interoperability, adherence to regulatory frameworks, and seamless data portability are all going to be key characteristics of connected clouds.

    12. Digital Transformation is going to be BIG in 2019

We keep saying this every year – and thanks to the rapid technological evolutions – the coming year is not going to be an exception. Apart from the advancements in machine learning and AI applications, the concept of ‘quantum computing’ is also set to pick up pace (market-leaders like Microsoft, IBM and Google have already started working on this). Unmanned aerial vehicles, or drones, are going to find more use cases, while the field of smart agriculture has many innovation opportunities (how about smart poles for agriculture?). In the corporate space, enterprise mobility management (EMM) and growing adoption of digital workspaces are the trends to look out for. Before 2019 is done and dusted, well over 55% enterprises will have ‘off-premise IT systems’.

Note: Products or services that have been digitally enhanced in some way or the other will be used by 1 out of every 2 Global 2000 companies, in 2020.

An increasing facet of digital transformation in 2019 will be the closer-than-ever inter-relations between AI/ML, edge computing and IoT. In particular, the growth of edge computing has a lot to do with the development of smart city applications (cloud-based data processing cannot be used for that). Edge computing delivers the real-time data processing required – and hence, automatically ensures optimal data utilisation. The number of data interactions between the cloud and the edge (in the so-called ‘Fog’) will go up manifold. One thing is for certain: the demand for newer, more powerful connected devices will continue growing exponentially.

     Major improvements in location services are also expected in the next couple of years or so. Maximum emphasis will be placed on sustainability, security and enhanced operational efficiency of the enterprise ecosystems. While the private sector will continue to be the main beneficiaries of innovative digital initiatives, the impact on the public sector (i.e., smart city applications, smart public utilities) will also increase. In a nutshell, digital transformation is going to disrupt how we work, how we interact, how we spend our leisure…indeed, how we live!

Machine Learning in 2019: Tracing The Artificial Intelligence Growth Path

Hussain Fakhruddin
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Hussain Fakhruddin

Hussain Fakhruddin is the founder/CEO of Teknowledge mobile apps company. He heads a large team of app developers, and has overseen the creation of nearly 600 applications. Apart from app development, his interests include reading, traveling and online blogging.
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machine learning 2019 trends

 

The age of the ‘intelligent assistants’ is well and truly upon us. Machine learning (ML) has already emerged as one of the key elements of global digital transformation – with cumulative investments (on artificial intelligence and ML) projected to reach $58 billion by the end of 2021. In the US alone, the market for deep learning software will jump from $100 million in 2018 to a whopping $935 million in 2025. The worldwide machine learning industry is growing at a CAGR of ~42% , and will be worth just a shade under $9 billion by the third quarter of 2022.

In the enterprise space too, the growth of machine learning use cases has been remarkable over the past few years. Total enterprise-level adoption of ML tools and solutions is expected to touch 65% before the end of the decade – and spendings will go up to $46 billion (according to a IDC report). On average, 55% corporate CIOs have identified ML as one of the core priorities for business acceleration. Over here, we will highlight how machine learning will continue to evolve in 2019:

  1. Newer use cases of ML are coming up

    Earlier this year, it was announced that the US Army will be using customised machine learning software tools (created by the Chicago-based Uptake Technologies) for predictive maintenance of combat vehicles. In other words, ML would be able to indicate when, and what type of, repair services a vehicle might require at any time. This ‘intelligent’ functionality will be powered by advanced sensors embedded in the vehicle engines. Yet another interesting use case of ML is the prediction of stock market fluctuations – based on the records of previous stock earnings. A recent research showed that such stock market predictions with ML have a 60%+ accuracy meter – which is impressive enough. Moving over to medical science and healthcare, ML models are being used to estimate the probability of death of a person (the accuracy in this case is well over 90%). Progresses are being made to expand the scope of ML further, in retail, marketing & sales, and industrial/manufacturing sectors. ‘Reading’ and ‘interpreting’ past data for forecasting the future – that’s the essence of machine learning – and the technologies are definitely getting more refined.

Note: The concepts of AI applications and ML tools are no longer limited to external robots. Instead, they have become natural extensions of business workflows and everyday applications.

  1. Adoption of ‘hardware optimised for ML’ set to rise

    2019 might very well be the year when specially prepared silicon chips – with custom AI and ML capabilities – become mainstream, at least for enterprises. The market for AI-optimised hardware will continue to grow rapidly in the foreseeable future. A series of new, powerful processing devices will be launched – and we would also get to see high-end CPUs and GPUs being used. Taken together, these tools and platforms will enhance the usability of ML hardware in a big way. In 2018 Q1, SambaNova Systems – an AI chip startup – raised a massive $56 million in a Series A financing round. By the end of 2025, global sales of AI-powered hardware will cross the $120 billion mark. The biggest of players – from Nvidia and Google, to IBM – are already in the game, and the machine learning hardware market will be one to look out for next year and beyond.

  2. Cloud adoption to rise with ML

    A yearly growth rate of ~25% will see the worldwide cloud computing market soar to $410 billion+ by 2020. The growing adoption of ML in enterprises is a key driver behind this surge. For the successful implementation of a ‘machine learning culture’, businesses have to focus on innovation more than ever – with particular emphasis on improved cloud hosting and infrastructure parameters. Over time, more and more ‘AI-specialised tools & systems’ (apart from business critical information and big data) have to be stored on the cloud – and the latter needs to have adequate security and usability standards for the purpose. A robust, scalable cloud support will help enterprises seamlessly move on from machine learning to deep learning, deliver greater value to end-users, and improve their ROI figures.

Note: Starting from 2019, the general user will start to get a clearer idea on how AI and ML processes work – thanks to the detailed ‘AI audit trails’. Given the critical nature of the domains (say: medical science) in which AI is making its presence felt, it is only natural that people would want to know how the technology arrives at its conclusions/predictions.

  1. Moving ahead with capsule networks

    For all the merits of neural networks, they often do not factor in the relative orientation or the position of select objects. As a result, ‘information gaps’ might remain in the machine learning models based on them. To tackle this, capsule networks have already arrived – and they are likely to replace many conventional neural networks in 2019 and beyond. In terms of performance, these capsule networks are a cut above the traditional neural network systems – with more accurate pattern-detection capabilities, and that too, with lesser data and a much-diminished probability of errors. What’s more – capsule networks do not require repeated training iterations either, to ‘understand’ variations. The size of the overall neural networks market will be more than $23 billion in 2024, and capsule networks will be right at the center of this growth.

Note: Advanced healthcare modules based on ML algorithms, for the comparison of medical images of a patient with that of others, are already being used. AstraZeneca, a biopharma company, has plans to use robotics and machine learning extensively – for developing smart diagnostics systems in China.

  1. Rise and rise of AI assistants

    Siri and Google Assistant and Alexa have become pretty much a part of our everyday lives, right? In another five years or so, the value of the worldwide AI assistant market will touch $18 billion. More importantly, each of the top ‘intelligent assistants’ are becoming smarter, on a year-on-year basis (on the basis of 5000 general questions, Siri managed to answer around 31%, among which nearly 80% were correct responses; in the same survey, Google Assistant answered over 67% questions, with an accuracy of a shade under 88%). With the scope of machine learning expanding, AI assistants are ready to move beyond the smart homes and users’ pockets. From the next year, Hyundai and Kia will start to provide built-in, AI-powered virtual assistant systems in their new car models. These assistants will be able to perform a myriad of tasks – ranging right from remote home and car control functions (through voice), to destination suggestions (based on previous preferences) and navigation guides. In all scopes of life, ‘intelligent assistants’ with ML capabilities will be making lives simpler than ever before.

Note: Smart chatbots (with artificial intelligence) are also witnessing rapidly rising adoptions. There is, however, cause to be wary – since biases in training datasets can cause serious damages in user-experiences. The ‘Tay’ chatbot by Microsoft is a classic example of such a failure.

  1. Developers will focus on solving more ‘real problems’ with ML

    When it comes to a fancy technology like artificial intelligence (multipurpose drones and automated surveillance cameras and self-driving cars, and the like), it is very easy to go overboard. However, it is important to realise that – while all of these things CAN become a reality – the steps towards a full-fledged data-driven ecosystem have to be gradual and systematic. In 2019, app developers and AI specialists will be eyeing to use machine learning to successfully address real, important needs (personal and business) – instead of simply churning out new prototypes of deep learning tools. Put in another way, developers have to understand that AI and ML are much more than just a couple of tech buzzwords – and when implemented properly, their potentials can be endless. There are many other technologies that are vying for attention at present (4d printing immediately comes to mind), and unless the developments in AI solve actual problems – investors might start looking elsewhere. It will be crucial to separate the ‘AI overhype’ from the ‘AI facts’, and act on the basis of the latter.

Note: In a recent study, it was found that 89% of all CIOs have plans to implement ML tools and applications in their businesses.

  1. World of the robots?

    Okay, that sounds a bit too dramatic, doesn’t it? In truth though, the roles of intelligent robots in workplaces are gradually increasing – and the improvements in ML are the primary cause for that. In Japan, three-fourths of all elderly care services will be delivered by AI-robots by 2025 – replacing human caregivers. Tianyuan Garments – a China-based t-shirt company – has plans to use ‘sewing robots’ at its Arkansas factory. In general, many labour-intensive tasks (particularly the repetitive activities that do not require much specialised skills) will be performed by ‘intelligent robots’ in the not-too-distant future. Apart from making workflows smarter, improving availability and reliability, and shortening the time-to-market, ML-powered robots would also significantly bring down operating expenses (as well as outsourcing costs, if any). Greater productivity should be a direct result of full-blown AI adoption at workplaces.

Note: Machine learning can also play an important role in precision farming. Smart poles for agriculture, with deep-root sensors and dedicated ML module(s), can help farmers take more ‘informed’ decisions.

  1. Voice technology to the fore

    Whether ComScore’s prediction of 50% of all search activities to be powered by voice by the year 2020 comes true remains to be seen – but there is no getting away from the fact that speech recognition (and interactions based on that) has emerged as an important element of machine learning. Unlike the early days of voice technologies, present-day speech recognition has a sub-5% error rate – which is more than serviceable. Interactive voice response (IVR) systems are becoming smarter than ever – thanks to iterative learning, and voice-based ML systems have the capability to transcribe a wide range of languages/accents. The trend of developers coming up with voice technology-powered mobile applications is also expected to gain further momentum in 2019. Already, assistants like Amazon Alexa and Google Home ‘understand’ our voice commands – and they are paving the way for more such platforms to enter the market.

Note: The traditional, suited customer service executives are also being gradually replaced by virtual characters. The latter offers more prompt responses – and since the conversation is intelligent (virtual agents learn from previous conversations), the personal touch is not lost.

  1. AI markets in USA and China – the big fight?

    North America has traditionally been the frontrunners, as far as artificial intelligence research and adoptions are concerned. This stranglehold, however, is growing weaker and weaker – with the Chinese market emerging as a serious force. In 2017, AI startups in China had a higher equity funding share than their American counterparts (48% vs 38%). The Chinese AI startup scene is holistic (unlike the slight fragmentations in the North American markets) – with the focus being on logistics, smart city projects, retail, healthcare, smart farming, and other domains. When it comes to deep learning too, China is clearly edging it – with 6X more patients issued than in the US. As per reports, China is looking to be at par with the American AI scene by 2020, and emerge as the undisputed leader of ML technologies within a decade of that. It will be fascinating to see how the US vs China race for global AI/ML supremacy pans out over the next couple of years.

Note: Instead of relying on third-party APIs, developers are increasingly turning to making their very own APIs for ML applications. There are plenty of developer-friendly assembly kits and mobile SDKs to provide the necessary help.

    10. More machine learning platforms (and better ones too?)

Platforms like TensorFlow, H2O, ai-one and Torch are already making a difference to how ML functionalities can be deployed in different scenarios. In the year coming up, we can reasonably look forward to more powerful ML platforms – with cutting-edge analytics, classification and predictive capabilities. The capacity of these platforms work with other APIs and big data will also continue to improve. The constant developments in machine learning are opening up opportunities for computers and mobile devices to ‘learn’ faster and ‘interpret/analyse’ data in a better manner. In a February 2018 Gartner report, the total available market (or, TAM) of machine learning at the end of this decade was valued at nearly $26 billion.

Note: AI/ML applications are also facilitating automated decision management practices. Informatica and UiPath serve as great examples of this.

    11. Revolutionising the way humans interact with technology

They might be present only in a handful of locations at present (<10) – but the ‘cashierless Amazon Go’ stores are completely changing the concept of shopping. In fact, by 2021, more than 2000 ‘Amazon Go’ stores might be present in the US alone. The manner in which we deal with, interact with, live with smart things (in particular) and technology (in general) is being shaped by the AI & ML revolution. Be it for a business, or for the society (read: surveillance cameras, smart city applications) or smart homes – deep learning is set to disrupt our lives everywhere, ensuring better performance across the board. Things that only seemed possible in sci-fi movies and our imaginations have been rendered possible with artificial intelligence. The key here has been the adaptability of the technology for different types of use cases. ML is solving problems and delivering value – and that’s precisely why it is growing in popularity.

Note: The development of ‘killer robots’ for warfare can be, potentially, alarming. A recent report predicted that the ever-increasing investments on AI for military applications might very well lead up to a nuclear war between 2040-2050.

     12. NLP to become more nuanced

As a sub-domain of artificial intelligence, the importance of natural language processing (NLP) has gone up significantly over the last few years. By the end of 2020, the global NLP market will be valued at well over $13 billion – with the industry CAGR hovering around the 19% mark. Primarily used for converting data into text, natural language generation is a key feature of many deep learning systems – and for the preparation of detailed market summaries or reports – NLP is extremely handy. The fact that natural language processing has also become highly accurate is also worth noting, and automated systems are being enabled to communicate ideas in a seamless manner. Cambridge Semantics and Attivio are some of the notable companies that provide NLP services.

Note: NLP modules typically need to analyse three things: syntax, semantics and context.

As more progress happens in the world of machine learning and new application areas get unearthed, the demand for AI specialists (rather than tech generalists) will continue to rise. This will, understandably, be accompanied by increases in their average salary figures. There are certain grey areas – like the prospect of mass unemployment and maybe intrusive surveillance – but it is safe to say, 2019 is going to be a big year for machine learning. AI-as-a-Service has arrived!

Sydney vs Singapore – The Race To Become The Next Silicon Valley

Hussain Fakhruddin
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Hussain Fakhruddin

Hussain Fakhruddin is the founder/CEO of Teknowledge mobile apps company. He heads a large team of app developers, and has overseen the creation of nearly 600 applications. Apart from app development, his interests include reading, traveling and online blogging.
Hussain Fakhruddin
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Startup scenario - Sydney vs Singapore

 

Silicon Valley and New York might still be the most attractive locations for startups, but the competition coming in from the APAC countries is growing intense. Over the last half a decade or so, total startup fundings in the Asia-Pacific have been on an upward trend, while that in the US have been slightly tapering off (venture capital funding was ~41% for both locations in 2017). China has emerged as a serious ‘startup haven‘ to reckon with – while both Singapore and Sydney (NSW, Australia) have buzzing startup scenes.

In terms of local connectedness opportunities for startup founders, both Sydney and Singapore are located in the top ten list of cities worldwide. While the former accounts for nearly half of all the startups in Australia, Singapore too boasts of well over 2000 startups. In fact, a 2017 Startup Genome report placed Singapore right at the top in terms of startup talent/innovation availability. In what follows, we will do a point-by-point Sydney vs Singapore comparison, and find out which of the two cities offers bigger advantages to startups:

  1. Sydney benefits from natural resources; Singapore focuses on innovation

    Compared to Sydney in particular, and Australia in general, the total volume of natural resources available in Singapore is much lower. The sheer extent of readily available resources give Sydney a headstart – a significant competitive advantage to the startups over here (particularly those in the primary sector). On the other hand, in Singapore – the emphasis is more on making the most from the relatively scarce resources. This is precisely where the importance of innovation – for engineering and biotech and advanced manufacturing – comes into the picture. Given its small size, the growth of Singapore as the most competitive Asian business nation has been nothing short of remarkable.

  2. Which sub-sectors are thriving in Sydney and Singapore?

    The multi-dimensional startup ecosystems of Singapore and Sydney make both of them great places to do business in. Fintech leads the way in the two locations: on average, 6 out of every 10 fintech companies in Australia are Sydney-based, while Singapore is home to nearly 300 fintech startups. The digital media business sector has also taken off in a big way in Sydney and Singapore – with the former serving as the headquarters of multiple leading media companies (the Australian digital media market will be worth >48 billion by the end of this decade). In Singapore, reports show that one-tenth of all VC funding in the last 6-7 years have gone to the digital media sector. Apart from these, adtech is the other dominant startup sector in Sydney (even though the growth rates of the advertising industry at the national level have been flat) – while big data & analytics startups in Singapore have received more than 5% of the total local VC investments. There is room for startups from different sectors to enter the two markets and earn big.

  3. Co-working spaces and the cost factor

    Both Sydney and Singapore have close to 2000 registered startup companies, with the Asian city-country having a slight lead (1800+ vs ~1600 in 2016). However, with close to 50 coworking spaces, Sydney has an edge over Singapore, where around 30 coworking spaces can be found. Australia is the more expensive place to start off a new business too with nearly 3X higher office rent figures ($107000 vs $37000) and considerably more pricey office spaces ($85/square feet in Sydney; $30-31/square feet in Singapore). The average corporate tax rates in Singapore (~17%) is also quite a bit lower than that in Sydney (can go up to 30%). The governments at both places play a proactive role in the growth of startups over there – with a multitude of favourable policies, tax benefits and other initiatives. For example, the ACE Startups Scheme in Singapore offers easy financial help for entrepreneurs, while the Entrepreneurs Infrastructure Programme of NSW tackles a lot of the networking & finance requirements of Sydney-based startups. The R&D tax incentives implemented in Sydney are also worth a special mention.

  4. Singapore marginally ahead of Sydney for fintech startups

    In 2014, Australia had less than 90 fintech companies. Cut to 2017, and that figure has surged to 590 – underlining the remarkable growth of this sector in the last half a decade. Nearly 60% of these companies are based in Sydney – making the ‘Harbour City’ the ‘fintech hub’ of the country. The fact that fintech investments Down Under are rising at a time of globally declining trends is all the more remarkable. Singapore has an even stronger fintech ecosystem – with close to $990 million being invested on this sector in 2017 alone (in 2016, Australian fintech sector received $670+ in the way of investments). Wealthtech, payments and lending are the three fintech startup categories that are growing the fastest in Sydney. Silot and InstaReM are two of the leading fintech players in Singapore.

  5. Business environment

    There is a lot in common, when it comes to the basic business legalities for startups in Sydney and Singapore (both taking features from the UK system). The two locations offer uniformly conducive environment for business – facilitating new entreprepreneurs to kickstart their businesses here. However, Singapore once again has slight advantages in this regard. While there is little to separate the countries in terms of ease of starting a business (in a World Bank report, Singapore and Australia occupy the 6th and 7th slots respectively) – Singapore offers more protection to minority investors, and has easier procedures for business contract enforcements as well. It’s not for nothing that Singapore has remained the ‘easiest place to do business’ globally right through this decade. Sydney, with all its startup-friendly policies and venture capital availability, comes in at the 15th spot in the World Bank study.

Note: The border clearance processes of Singapore have also been recognised as the most transparent and efficient in the world.

  1. Singapore has the ideal infrastructure; Sydney has some catching up to do

    From space allocation and mentorship, to incubation and business acceleration, the government of Singapore offers specialised assistance to startups at various stages of growth. The Australian government (the NSW authorities in particular) have a similarly supportive attitude – but according to many entrepreneurs, there are still gaps in the day-to-day interactions between the government and the Sydney startups. What’s more, life is just that bit easier for venture capitalists in Singapore – since funds can be obtained from the government, with an expected return as low as 5-6% (in other wards, Singapore has ‘more VC funds’ than Sydney). The Australian startup hub definitely has the more naturally innovative minds – but the inherently risk-averse nature of many Aussie investors are somewhat holding things back. Political stability, peace and timely assistance are bolstering the startup scenes in both Singapore and Sydney – and at present, these helps are making a more telling effect in the Asian island nation.

Note: A couple of months back, AirTrunk – a fast-developing Singapore startup – raised $850 billion as funding, for a business expansion in the APAC region. Incidentally, the company also has centers in Sydney and Melbourne – and has plans to pump in funds in these centers too.

  1. What are the entrepreneurs thinking?

    Understanding the mindset and the thought processes of the business entrepreneurs/founders at any place is a great way to predict how the startup scenario will pan out over the long-term. According to the 2018 Startup Genome report, more founders in Singapore has the typical ‘entrepreneur mindset’ (32% vs 24%) – while those in Sydney have a slightly greater inclination to emerge as ‘business builders’. In terms of ambition, drive and hunger for success though, Australian business owners are a step ahead of their counterparts in Singapore (31% of Aussie founders have high ambition levels, while 56% of them wish to make a difference to the world; the corresponding figures for Singapore founders are 18% and 49% respectively). The naturally innovative nature of Sydney-based startup owners becomes apparent by the fact that only 30% of them have ‘relevant experience’ in their respective sub-sectors. In Singapore, this figure is as high as 44%. One thing is pretty much clear – neither at Sydney nor at Singapore is money-making the sole prerogative of the startup-owners. Many of them actually want to ‘change the world’ with their business.

  2. Tax rates and incentives in Sydney are great; but Singapore is even better

    Apart from the much-lower average corporate tax rates in Singapore, the Asian location also has the more favourable income-tax (IT) structure. The cap on the progressive personal income taxation in Singapore is at 22%, while in Australia – the IT rates can go up to 45%. The single-tier corporate tax system in Singapore is also simpler (and rules out chances of double taxation) – while in Australia, dividends are also taxable, according to the ‘franking credit’ (tax amount paid by company) mentioned in the statements. It also has to be kept in find that the 43.5% refundable tax for Aussie startups with turnover <AUD 20 million (under the R&D tax incentive programme) is a major incentive for people looking to start businesses in Sydney. There is, however, a difference in which foreign-sourced funds are managed by the respective governments. While resident Aussie companies have to pay taxes on profits earned anywhere in the globe – the profits earned outside of the country are not taxable in Singapore.

Note: In terms of enabling trade (openness to trade), Singapore emerges as the clear winner, While it has the top spot in a 2016 report, Australia is listed at the 26th spot.

  1. Where are the more knowledgeable startup founders located?

    Once again, the numbers are close – but it seems that Australian entrepreneurs, on average, have greater business knowledge than those in Singapore. A recent report pegged the ‘theoretical know-how index’ of Aussie founders at 5.8 – significantly higher than the 4.9 index for Singapore-based founders. In terms of practical know-how too, Sydney edges it – albeit the fight is closer (5.7 vs 5.3). The metropolitan GDP of Sydney (~$335 billion) is much higher than that of Singapore (~$270 billion). This automatically means that the size of the local market is greater in the Australian city. In Sydney, founders also have a greater sense of being part of a community (‘sense of community index → 7.5’). In both the places, founders regularly interact with each other, and strike up mutually beneficial strategic partnerships.

Note: The average salary of a corporate professional in Singapore is around the $3100 figure, slightly lower than the average salaries of $3500-$3600 in Sydney.

     10. Availability of qualified workforce

Human capital is the biggest asset of any startup – and there is no shortage of highly-qualified human resources in either Sydney or Singapore. Nearly half of the total employable population in Singapore hold advanced degree certificates (or diplomas) in their respective fields – while 4 out of every 10 members of the Australian workforce can boast of having tertiary qualifications in their CVs. Finding and recruiting suitable, qualified workers is fairly easy at both places. However, the differences in the work hours and the minimum wage (nothing specified in Singapore; AUD $17.70/hr in Australia) have to be taken under consideration. While Singapore does not allow people to work for more than 12 hours in a day, the maximum work-hours in a week for Australian workers is 38 hours.

Note: The procedures for registering and protecting Intellectual Property Rights (IPR) are more or less similar in Sydney and Singapore.

       11. The flow of startups from Sydney to Singapore

The recent trend of Australian startup companies to expand in Singapore – with a view to nurture and grow their businesses – is interesting. While there is no doubting the merits of the startup ecosystem in Sydney (and, of course, the government initiatives), experts feel that the tax incentives and other assistances are targeted more towards companies in their absolute nascent stages. For startups that are a bit more established, Singapore is probably the easier market to tap into, for funds and collaborative networking, and even customers. There is just a bit of additional bureaucracy in the research support incentives in Sydney, which is not present in Singapore. However, as multiple entrepreneurs have confirmed, startups are NOT LEAVING Sydney in favour of Singapore. Instead, the Aussie companies are looking to streamline and speed up their growth by strengthening their presence in the Asian market.

Note: The Dream Collective, Shootsta and HashChing are some of the major Australian companies that have initiated plans to expand into Singapore.

Sydney is a relatively peaceful city with a charming lifestyle – which adds to its attractions as a startup haven. While Singapore’s lifestyle is probably not as alluring, the stability and peace in the country offers encouragement to entrepreneurs. The inflow of foreign direct investment (FDI) is, understandably, much higher in Sydney, unemployment rates are lower, and the cost of a single-bedroom house is roughly the same at the two places.

Sydney, as has been pretty much well-documented, is a terrific place for launching a startup – particularly tech startups. However, from our discussion above – it is pretty clear that Singapore has certain extra advantages for new businesses. Both the places are certainly in the race to become the ‘next Silicon Valley’, and it seems that Singapore has taken a slender lead in this race.