LK Weekly Precis - Corporate investors, monetising 5G, and hustle to complete deals before the slowdown..

March seems to be starting on a higher note with several startups raising late stage funds in th range of US $1 billion and more. This includes Grab, Go Jek, NYSE listed Sea Group in the recent follow on share offer and the Alibaba backed, Chinese influencer power blogger marketing platform, Rhunn.

Government initiatives are picking up steam especially in Southeast Asia for coaching services, co-working spaces, tax exemption, seed fundings and other resources but requires program owners to improve execution, assesment criterias and reach for better outcomes.  

Here are some key highlights to note this week;

5G Opportunities for Operators and Startups

The recent MWC 2019 in Barcelona, Spain may have been a routine yearly event, but visitors certainly caught a glimpse of a very different future for communication sector in the coming years. Change in business model, operation, partner eco-system, new customer and service segments is imminent. Operators will need to move upwards of the infrastructure, basic voice and data services for revenue and quicker ROI or risk running into massive losses at the point of the next network upgrade.

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Various participants including KT, China Mobile, startup communities and others demonstrated usecases on Smartcities, Smartfactories, 5G Cloud, Augmented Reality and Virtual Reality connectivity, autonomous vehicles and many more, that is pointing to enterprises as the key segment to monetise 5G investments.

Traditional partners such as Samsung and LG are adding new IoT services to offerings, apart from a range of mobile and connected devices. For instance the grocery replenishment service with Samsung refrigerator.

Telco operators in Korea, Japan and China are certainly leading the race when it comes to implementing 5G use cases and without a doubt will be in the forefront of innovations, in this space. 

AirAsia launches Redbeat Capital in collaboration with 500 Startups

In our last weekly summary we covered how DBS was looking to invest in startups that can help distribute the banks products further into new territories. It seems that trend is here to stay with more conglomerates taking the same approach to unlock new markets and innovation.

This week AirAsia announces the launch of Redbeat Capital in collaboration with 500 Satrtups. The US$60 million  fund will be used to provide post seed funding for global startups making way into Southeast Asia in travel, lifestyle, logistics and fintech segments.

Huawei Cloud Region Opens in Singapore

Huawei adds a new cloud region in Singapore aside from China, Europe, Latin America, Hong Kong, Russia, Thailand and South Africa. Huawei Cloud now has 40 availability zones in 23 geographic regions. Offerings will include various platform services for artificial intelligence and machine learning.

Currently the company is actively hiring the regional team and is aware of the market's highly competitive landscape with several key cloud vendors already delivering values, where customers are rapidly adopting cloud for better IT efficiencies, investment flexibilities, consistent performance, geographic expansion and faster time to market.

In the past, Huawei depended on its portfolio of foreign based Chinese customers to penetrate into new markets, but we should anticipate some new field tactics beyond price cuts and equivalent services for businesses this time. A shift of sales focus on nailing at least sixty percent of revenue share from services that run above the basic compute, storage and networking infrastructure services is almost mandatory to differentiate from the rest of the pack.

Meituan-Dianping and Chope

Restaurant booking sites and apps in the region has certainly changed how restaurants perceive customer experience for better or worst.

On peak days, customers are rushed to cater next booking, cancellation fees for no show, constant interruption from servers to top up drinks and other add ons to keep the table, have just made it more of a hassle for diners lately.

However, this deal with Meituan-Dianping should particularly benefit Chope to increase utility of their app and tap into the chinese tourist market at the same time. But will these reservation and restaurant referral sites in anyway add value to diners experiences? Can they help restaurants create unique experiences with the data they are collecting?

Horizon Robotics Raises US$600 Million

The trade war is now opening up opportunities for AI chip makers from China including Huawei and Alibaba to accelerate release of products within this year.

Horizon Robotics is one of the highest valued unicorn in China currently, apart from Cambricon for developing AI chips. The company recently raised another US$600 million in funds to push through development, final designs and outsourcing of manufacturing processes.

With several Chinese AI chip makers planning to outsource manufacturing process and rush to release second generation chips by mid 2019, this could prove to be a prospeporous year for Taiwan based TSMC with a full factory load.

Some Cheers for Startup Communities in India

Finally some cheer from our startup entrepreneur communities in India as the Department for Promotion of Industry and Internal Trade (DPIIT) in India announces changes in the definition of startups (turnover not exceeding Rs 100 crore) and set the confusion over 'angle tax' to rest.

Fintechs Refining Playing Field

Lastly fintechs everywhere in the region are refining market strategies to bridge cashless payment, cryptocurrencies, lending, mobile wallets, e-wallets, and other financial services for both consumers and businesses through new alliances, effective sales programs and much polished product releases. 

Aside from startups, fintechs are increasingly seen as a lucrative attached revenue source for mobile operators, ecommerce platforms, conventional financial institutions and travel related sectors that has access to a broad audience of B2C and B2B buyers. 

Some noteworthy highlights of fintech activities this week are as follows; 

• Axiata and Singtel collaborating for cross border payment;
• Alipay having reached staggering 2 million users and 50,000 merchants in Hong Kong in just a year from launch;
• PayTM India introducing subscription programmes to increase utility;
• Razer launches beta services of Razer Pay digital wallet in Singapore;
• and Mobi Direct teaming up with Worldline for digital payment processing in Pakistan.

Some common trends persist and still an untapped B2B segment.... 

Overall the startup scene is still evolving around ecommerce, ride-hailing, logistics, travel, gaming, payment and other B2C segments where a majority of funding deals are channeled by investors at the moment. As a result, we continue to observe several common repeating themes from previous weeks as follows;

• Traditional sectors such as finance, telco and travel continue turning to startups eco-system to accelerate innovation, build new growth engines and discover business frontiers. 
• Chinese corporate investors such as Alibaba, Tencent, Didi and Meituan-Dianping continue to supply capital to various Southeast Asian startups in ride-hailing, travel, e-commerce and other lucrative B2C segments. 
•  Cloud companies such as Facebook (with IMDA) and Alibaba are working in deeper collaboration with regional startup incubators to lure and accelerate startup success on their platforms.

Nevertheless, this is an ideal period for startups in the region to reboot the drawing board in the B2B segments, leveraging the approaching 5G connectivity in IoT, augmented reality, virtual reality and smart factory arenas for new innovations. 

Telco operators in Southeast Asia are generally less prepared to monetise 5G services and may be more willing to pour investments in a startup ecosystem that fills the service gap in a shorter span of time. 

In addition, many of these telcos are positioned poorly for an internal transformation in terms of adding skills, reorganising business structures and constructing business capabilities for 5G use cases due to outdated business policies, practise of privilege systems and lack of diversity in workforce.  

AI Playbook for Startups and SMB

The AI Playbook for SMBs and Startups

Most businesses in the SMB and startup class, still struggle to grasp the broad definition of AI disciplines, what AI really is to their business, where to get started and what successful execution looks like for their businesses. Simplifying a focus area narrowing these concerns specifically to address your business pressures is helpful to keep team in line with long term business goals and strategies.

There are many AI Playbooks written by renown gurus such as Andrew Ng, meant for Fortune 500 organisations and they do contain some very useful guidance and tips for all businesses. Nevertheless, smaller ventures with limited funding, skills and resources may need to rework tactics to suit business.

Here we present steps to formulate a generic AI playbook meant for small and medium sized businesses, which includes stages to:

• internalise AI definition for the business; 
• outline and prioritise business problems that can be addressed with AI;  
• experiment with ready pre-trained model
• build required skills or internal structures;  
• prepare training and test data; 
• choosing a path between pre trained and custom built model for solutions, 
• and assessing overall impact to businesses performance. 

The generic AI Playbook should ideally incorporate other dimensions unique to your business, industry and commercial eco-system over time, for maximum results.

Define and internalise AI for your business...

AI itself is a broad subfield of computer science. It encompasses many branches of studies including rule based AI and decision trees; machine learning (regression, classification, neural networks/ deep learning, reinforcement learning, supervised and unsupervised learning); robotics; text and speech processing (NLP, NLU, NLG); vision (computer vision, image recognition) and several other applications that still have not left the laboratories. 

AI is a field where one or several of these approaches are used to create machines, agents and models that perceive, learn and adapt to perform various tasks and cognitive function similar to humans.  

The AI domain is practically exploding with hundreds and thousands of new approaches, methods and solution areas in transfer learning (learning from lesser data and smaller models), networks with memory (generalising AI, e.g. DeepMind’s differentiable neural computer), reinforcement learning, training simulations, hardware for training and inference that are rapidly advancing in the research, development and real world application stages. 

Communicate how AI should be perceived in your business....

As such, it's easy for businesses to get lost in an ocean of offerings that comes with vendor specific methods, best practices, strategies, tools and ready use cases. Charting and communicating how AI should be perceived within the business very early on, in your AI journey is crucial to ensure potent utility, informed expectations and performance. 

For instance should AI merely assist workers to perform better at tasks? Should AI considered as an avenue to outsource cognitive functions otherwise performed by human workers ? Or should AI be a form of cognitive technology to help expand ordinary worker's ability to explore meaningful solutions to business problems both creative and logical?

Setting the right internal tone on AI can guide alignment with work culture, strategies, high impact projects, investments, selection of tools and services that can drive AI values across the business.

Define business problems that can be solved with AI

The common misconception in this stage is, data science or business team alone is responsible to outline business problems solvable by AI. 

Every business unit can contribute to this stage including sales, marketing, services and tech departments. Imagine how it will affect your business if tech can reduce 40% of cooling needs in your datacenter or how your customer service or inbound sales department can improve performance by multi-fold with realtime speech to text analysis that prompts them with emotion, sentiment and other useful information during a call with a customer.

Build working team; conduct regular meet-ups; answer business questions around growth hacking, improving or staging customer experience, workforce productivity and innovation; keep a revisable record of issues pressing the business growth (this can be a simple excel sheet with list of items); prioritise problems to solve; assess data availability; experiment, assess skills requirement; select initiatives for executive sponsorship and create projects to execute. 

Assess data availability and prepare training data for the problem

Data sources...

Data can be gathered from within the business, public datasets, procured from external sources (e.g. Image Net, Google Data search) or a mix of these. Identify useful data assets for your business problem, list what data is missing or not available and which data can be excluded from your raw selections. Collect and aggregate sufficiently to fit your model. 

Data preparation is a significant contributor to cost of building AI solutions specifically ML models, aside from high performance computing resources though GPU innovations has brought so much economics since a decade ago.

Clean and transform data for training...

Most ML models requires large amounts of data to train and converge before they can be applied to business. Preparing these data may require domain experts to collect raw data, identify features with strongest prediction power and label them. Though the business problem you are aiming to solve, tools and selected algorithms ultimately influences data preparation approach and mechanisms. 

Data transformation is revisited several times....

It is common for developers to spend 50% or more time preparing and scrutinising data quality and revisit data transformation processes (or feature engineering) several times during the development of the model. 

Split data sets for training, test and validation...

The prepared data is usually split into training set to fit the model, test set to evaluates final model fit on the training dataset and validation set to evaluates model fit on the training dataset while tuning model parameters.

Consider pre-train models

Ready to train models for various use cases...

For common business problems which requires simple recommendations, classifications, regression, clustering, anomaly detection and ranking, look for pre-trained model offerings by public cloud vendors such as Google, AWS, Alibaba and Microsoft. Many large and niche AI vendors now offer pre trained models for other slightly complex solutions to perform predictions, speech processing, computer vision (video, image and object recognition), decision support and planning tasks. 

No need to build algorithm or ML models.....

These offerings saves businesses the hassle of building algorithms and models from scratch. Plus it's a great way to get started with applied AI for business with quicker results and fast ROI times without having to reinvent the entire process (the value is in the model outcome not building it, especially for non-tech businesses). 

No need to replicate work for  ML backend .....

The team need only concentrate on preparing data, select features and train the model for required tasks (e.g predictions, classifications).  Trained models/ functions can then be deployed with other production applications and systems through APIs provided by cloud vendors to apply learnings on new data samples without replicating any work for ML backend.

ML and DL frameworks

In the event that the business problem you aiming to solve is much complex and does not have a ready pre trained model available, it is advisable to custom build the model custom build the model on a machine learning and deep learning frameworknof your choice, offered by cloud vendors through their ML-as-a-service offerings.

Frameworks such as Tensorflow, Pytorch, MXNET, Keras, Caffe, Scikit, Microsoft Cognitive Toolkit and Theano, comes readily packaged with interface, libraries, tools, pre-built and optimised components to facilitates fast development of AI models without getting into the details of underlying algorithms and complex architectures. 

New developers can benefit from starting with frameworks such as Keras and delve deeper into Tensorflow or other suitable frameworks as they get accustomed with the building blocks and programming languages.

However, it is highly recommended for new teams to get experience from simpler projects involving pre trained models first before venturing into challenging custom building models. It is easy to loose ROI when tinkering with ML or DL models without past experience and benchmarks.

Building AI skills 

When it comes to smaller businesses, building AI skills in-house is really a trial and error exercise to find the right blend of subject matter experts (or the minimal quo) to address AI problems and build solutions. This team's skill level is adjusted according to how they plan to address business problems, for instance with ready models or custom build models.

AI requires programming (e.g. R, Python, Lisp, Prolog, Scala, C, C++ and Java) data science (at least fundamental level) and business domain knowledge at the minimum to obtain some high level results. A strong foundation in mathematical fields such as probability, statistics, linear algebra, mathematical optimisation is necessary if your business wish to develop own algorithms or modify existing ones to fit specific goals and constraints.

Training existing workforce, hiring from other organisations and university grads are some of the common methods to build the right team.

Education sites such as Coursera, EDX, Machine Learning Mastery and many others provide neutral self learning content suitable for both technical and business audience. Content varies from fundamental to advanced level and comes with certification process that can be committed at one's own pace. 

Vendors such as AWS, GCP and Microsoft too have their own academies but syllabus may be skewed towards respective services. AWS and Microsoft are so far the best corporate academies I encountered and complies to many criteria mentioned in Andrew Ng's Fortune 500 AI Playbook in addition to easy to follow content and no fuss lab use (note that this is paid resources).

Assessing impact to business..

Once trained, tested and deployed, weigh the model's impact to your business performance. I use a no fuss method where I categorise what was increased, reduced, created and eliminated over a set period of time.

For instance:

• Did the model INCREASE overall customers value, revenue or profitability ? 
• Did the model REDUCE fraud risk, customer churn or cost ?
• Did the model CREATE new growth engines, data products or differentiators ? 
• Did the model ELIMINATE manual task, blind spots or redundancies ?

These answers are then scored and quantified for best alignment with overall strategic goals and objectives. The data is then collated for all AI projects to visualise and quantify the total impact to business financially (profit, revenue, growth rates, etc) and other areas such as competitiveness, reputation, and customer satisfaction. 

This exercise is helpful in two or three ways :

• to identify successful AI projects and approaches
• to build AI projects that can increase the value or performance of existing models
• find optimal opportunities to apply AI learnings to other functional areas 

There are many ways to measure impact, choose one that you are most comfortable with and put it to work. Use the discoveries to increase effectiveness of AI projects for your business while filling gaps in skill sets and access to useful data assets.

Conclusion - Ethics, Explainability and Human-Machine Interaction

In the end, AI systems are nothing but stacks of infrastructure and programs in various architectures that learns and executes continuously, depending on its utility. The machines aid human workers to predict outcomes, navigate a situation and decide on best action path with less mental drudgery while spanning solution options that was never possible before.

But that doesn't mean AI systems and models are perfect or free from self vested exploitation. Since the 2010s public concerns about racial and other biases in the use of AI for criminal sentencing decisions, creditworthiness and other forms of social credit systems (e.g. China) is driving demand for transparent AI systems and agents.

Explaining decisions of AI algorithms or models, especially those powered by deep learning or other complex neural networks is still a difficult task and is also known as the 'interpretability' problem.  Unlike decision trees and Bayesian networks that are more transparent to inspection, complex neural networks stack on one another and may involve millions of neurones processing towards the outcome.

As such, deploying sufficient governance protocols for data privacy and security; an architecture which facilitates transparency and tools to detect bias in algorithms is as critical as creating successful models or functions. Though in many cases engineering for more explainability and transparency may lead to significant accuracy trade offs. 

Making Sense of Public Cloud Investments

The public cloud infrastructure services (Iaas) market, according to Gartner's estimation is reaching $60 billion in 2019 and is growing at 27% YoY (fastest growing cloud services segment) from the previous year. Assuming Asia contributes 25% of the global share, turns the region into a critical geo for cloud vendors to battle for market leadership. 

However, cloud practitioners in Asia are still somewhat battling with what is considered as decade old cloud myths evolving around security, compliance, IT control and short term ROIs when conversing with regional customers riding on conventional structures and work cultures. This includes financial institutions, communication service providers, retailers, manufacturers and other asset intensive industries.

While these are all valid concerns, over the years premium cloud vendors have more than proven that their infrastructure and platform services (IaaS, PaaS) are superior to any single institutional  environment in terms of performance, security, reliability and overall economics to sustain just about any type of workloads. The public cloud values are now evolving rapidly with introduction of newer, advanced services in opensource databases, artificial intelligence, low code coding, and running mission critical workloads, enabling businesses to gain better control of operations as they cut waste, optimise performance, develop the much needed new capabilities and differentiators faster than ever. 

Open-source Database Services

Businesses have been dabbling with open-source databases (as well as open-source operating systems, libraries and tools) even before cloud computing services emerged and was aware of the potential to free organisations from growing commercial database licensing burdens. However, managing these open-source databases required additional training, operational and maintenance resources, apart from the unpredictable fixes and releases from the open source community. These issues limited the usage of open-source databases such as PosgreSQL to a small number of non critical, in-house development projects and applications in the past. 

This scenario changed in the recent years as cloud computing companies started to offer fully managed open-source database services such as PostgreSQL, MariaDB, MySQL and many others. Businesses start to increase adoption overnight by consuming cloud based open-source database services for existing and new applications while gaining new efficiencies, never possible before. The polyglot nature of cloud native development, where multiple types of databases and models are deployed in an application composed of micro-services and API calls only drives this trend further. 

Popularity of open-source databases according to DB Engine Ranking site, is about to converge with commercial databases in 2019 or in the early parts 2020. 

Artificial Intelligence, Machine Learning, Deep Learning

Explore pre-trained ML models

If new to artificial intelligence and its sub domains machine learning, deep learning and transfer learning, take advantage of the various pre-trained models offered by public cloud vendors such as Google, AWS and Microsoft to perform prediction, classifications, natural language processing (NLP), speech processing, computer vision (video, image and object recognition), decision support and planning tasks. 

Start by experimenting solutions to business problems while inflating returns on organisational data assets. This provides an opportunity for the business to quickly reap benefits from readily available models where the team need only prepare data, select features and train the model for required tasks (e.g predictions, classifications).  Trained models can then be deployed with other production applications and systems through APIs to apply learnings on new data samples.

Building custom models..

As the internal team gain experience and get familiarise with ML projects, commence a process to create step-by-step AI playbook to define and prioritise business problems that can be addressed with AI, data preparation, algorithms selection, and developing a model to make predictions. This exercise should involve critical members from business, data science and the engineering teams, ideally aligned to addressing growth challenges, resources optimisation, innovation and enhancing customer experience. 

Machine learning and deep learning frameworks such as Tensorflow, Pytorch, MXNET, Keras, Caffe, Scikit, Microsoft Cognitive Toolkit and Theano, that comes readily packaged with interface, libraries, tools, pre-built and optimised components facilitates fast development of AI models without getting into the details of underlying algorithms and complex architectures. 

Pick a cloud vendor for ML-as-a-service that best fit your needs...

Top public cloud vendors such as AWS, Microsoft Azure, Alibaba and Google provide support to most of the frameworks mentioned above for custom building models apart from their pre trained model offers for common business problems. New developers can benefit from starting with frameworks such as Keras and delve deeper into Tensorflow or other suitable frameworks as they get accustomed with the building blocks and programming languages.

Develop cloud native applications faster ...

Today a challenge sparks an idea, an idea turns into a prototype and a prototype transforms into a multi-platform first release in just matter of days and weeks. Aside from core infrastructure services, cloud vendors offer various advanced services ranging from devop, language SDKs, container orchestration, serverless computing, no to low code development platforms (both proprietary and open-source), APIs, code libraries to ready templates to help organisations accelerate application development arising from sudden business needs. 

Most post cloud applications are series of micro-services connected to one another via APIs leveraging languages, databases and code bases most suitable for the required function and the supporting technical environment.

No to Low Code Platforms

In addition, low code platforms such as Mendix, Outsystems, Google App Maker, Appian, and Microsoft Power App empowers traditional developers, IT professionals and to a certain degree the average business users to accelerate software delivery for business use cases made up of common features and components by simply utilising existing templates, forms, objects, drag and drop of prebuilt features.

Ensure performance of mission critical applications....

Learn which of your mission critical applications are suffering from intermittent or persistent performance issues affecting business processes critical to productivity, customer experience and revenue activities. Cloud vendors offer various services and pricing plans to optimise business applications from SAP, Oracle, Microsoft, IBM and others where data grows exponentially with transactions in time.

Exploring public cloud offers for the aforementioned type of applications can reveal credible areas of savings, operational simplification, performance improvement, speedy delivery of new business requirements, higher utilisation, improved security and compliance. Keeping a revisable record of requirements and working with cloud vendors to formulate cloud contracts to efficiently counter all planned and unplanned workloads can free internal resources for myriad of higher impact business activities.

Know values, learn constraints and strategise for long term...

Ultimately cloud values can vary significantly from business to business depending on the nature of operational model and the overall ecosystem. The four areas mentioned here are general enough for most sectors, though some businesses might benefit immensely from other emerging tech such as augmented reality, IOT, 3D or bigdata platform services.

In addition, identifying constraints that prohibit enterprise architectures from extracting full potential of public cloud eliminates aimless unproductive explorations. For instance legacy apps on rigid centralised architectures that may not gain much efficiencies when moved to the cloud.

Finally having a solid long term strategy, helps. For instance all new development and deployment to take the 'Cloud First' approach, or maintaining a hybrid cloud environment with only unpredictable workloads moving to public cloud or 'Cloud Only' approach where the internal teams build in-house brokerage capabilities to fully monitor its environments on multiple public clouds and perform various optimisation as needed.

Will Data Privacy spark the next Wave of Innovative Applications?

Privacy is a top agenda as we live in an era where data is a fundamental requirement to access healthcare, education, financial facilities, commercial services, employment, security, welfare, social networks, media and even exercise voting rights. Everything around us is designed to optimise and improve by harnessing data, that it has become an undeniable asset with unprecedented utility. 

This makes data a prime heist for unethical hackers and modern day criminals who steal, doctor and manipulate it for illicit purposes including financial gain, scams, terrorism, identity theft, spreading fake news or spying. Some notable breaches from 2018 are the Aadhar breach involving a staggering 1.1 billion records, forum site Quora, Googleplus, Facebook, several airlines and other online entities.

This is why many large corporations that gauge petabytes of user data are under heavy scrutiny by privacy enforcers and regulators such as National data protection authorities in the European Union through the much debated GDPR guideline. For instance a recent announcement by one of Alphabets subsidiary, Sidewalk Lab on a plan to package and sell cellphone data for various service enhancement sparked outrage from various ethics observer and human rights groups, even when the company justified that the unique identifiers will be removed from the dataset.

Staying connected at all time and living somewhat transparently, is the future reality...

But harnessing data is an unavoidable exercise when it comes to achieving greater quality of life, good decisions and optimal utilisation of natural resources.  As such, refraining from the internet, social networks, online media and performing digital transactions will only result in disadvantages, inconvenience and missed values from economic, social and safety standpoints.

Even if we successfully retained a mysterious existence, we will not be free from unwarranted surveillance by both businesses, independent institutions and governments in public places for various reasons as cities, buildings and public amenities become embedded with sensors, camera feeds and chips that connects to the Internet or clouds to perpetually collect data and create values.

Privacy in Asia....

Asia witnessed multiple incidents of data breach last year among CSPs, healthcare providers, financial service providers and even government sources that lead the public to question credibility of domestic institutions in protecting personal, usage and behavioural data. According to a report by Gemalto, Asia Pacific region contributed to over 35% of cybersecurity incidents last year. 

In most cases, institutions took no further action to restore user confidence apart from the breach announcement, which is a mandatory compliance requirement. Gemalto further stated that the numbers could have been much higher in reality, due to unreported incidents especially in Southeast Asia. Singapore is an exception to this as the authorities take great pride in concluding data breach cases successfully to safeguard the regional digital economy.

Asia also suffers from an overall weaker legal framework and laws for privacy though dedicated privacy task force is emerging in India, Thailand, Indonesia, Singapore and Philippines, out of which Singapore perhaps is the most advanced in ratifying and enforcing the aforesaid laws. 

At the same time, there is a rise in governments that are explicitly developing and extending state surveillance under the pretext of national agenda, security and cyber crime laws for political reasons. Technologies such as facial recognition, artificial intelligence, biometric, advanced identity card systems, ever increasing compute capacities and various citizen facing applications are converging into social scoring systems that provides granular monitoring of individuals, regardless of their consent. 

But there is a new way to tackle privacy - a new form of application architecture is on the rise .....

Apart from comprehensive legal framework, enforcements and the use of advance security architectures, strategies and tools, the application design and architecture itself can serve as a mechanism to protect privacy by separating personal data and identities from applications.

For instance, a project led by Led by Tim Berners-Lee called 'Solid' and is currently run from MIT, consist a set of tools and conventions that helps to preserve integrity of identities, privacy and data ownership while enabling developers and businesses create a new wave of innovative web applications. Solid is a web decentralisation project that aims to put rightful ownership of data back to every users and empower applications that are completely decentralised. 

Currently the use cases are limited for developer community alone, though discussions on Solid servers and tools are beginning to emerge in Quora, Reddit, FB Groups and GitHub.

Similarly Digi.me and HAT are all projects aiming to decentralised applications on the web, where users can create libraries of their data and is completely in control of how the data is used by corporations and governments.

Privacy concerns will lead to new opportunities ....

Most large businesses perceive privacy as a critical challenge to overcome with growing pressure from regulators. Though regulators approach are not helping as solutions developed around privacy laws are difficult and expensive to attain technically, especially for Internet giants.

In the end, solution might just emerge from the tech world itself through new architectures and business models lead by smart startups willing to embark on new frontiers such as Solid, Digi.me and HAT to develop fully decentralised applications that satisfy users, businesses and regulators.

Larger companies might use 'business within business' approach to innovate in the privacy space or quickly get onto the new themes through acquisitions, mergers and rethinking business models. On the another note, decentralised web may lead to even larger paradigm shift, similar to the ones we experienced when organisations moved from on-premise to cloud of everything, creating new digital eco-systems altogether. The one thing that no one can afford to do, is to remain unchanged.

'Chip War' in search of AI Supremacy!

Ever wondered why we need GPUs or AI accelerators for optimal performance of AI Applications? Ever wondered why a field that existed since the the dawn of twentieth century is only now burgeoning with breakthroughs? 

As Kaplan and Haenlein puts it, "AI is a system’s ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation”.

Artificial intelligence applications that mimics cognitive functions involves deep neural networks, machine vision, sensor technologies, and machine learning techniques that depends on multi core designs, low precision arithmetics and in memory computing capabilities to work well in both cloud and the edge.

In fields like robotics, autonomous vehicles, drones, decease diagnosis, speech recognition, object or face recognition, capability to vastly expand AI calculations by embracing massively parallel processing power, means near real-time learning, energy efficiency and optimal performance. This is why AI applications requires AI accelerators in  its infrastructure design to support various types of computing based on the use cases for sustaining performance during training and inferences.

AI Accelerators

Researchers in scientific fields were probably the first to discover, experiment and adopt GPUs for accelerating AI applications, almost 2 decades ago.  Since then, a 'Chip War' has slowly formed along with other technological developments and is now reaching its peak, among cloud companies, Internet giants, chip makers and startups searching for supersonic growth, especially in the fields of artificial intelligence, machine learning and deep learning. 

Apart from GPUs (graphics processing unit), other types of microprocessors available in the market currently are FPGA (field programmable gate array), ASIC (application specific integrated circuits), in memory architecture and heterogenous computing. As of today, there is no dominant design that underpins all AI architectures, but NVDIA do seems to command some leadership here while others such as Intel, AMD, WAVE Computing, UK's Graphcore and Israel's Habana Labs  are all beginning to ship viable products. 

Chinese players...

China has been investing aggressively to produce indigenous AI chips to wean from dependence on US based producers, with ongoing researches underway by major tech corporations, such as Baidu, Alibaba, Tencent and Huawei, apart from government initiatives. 

Huawei released several products last year, claiming to be faster than NVDIA with ready adopters in datacenters globally. Another startup called Cambricon released products and is aiming for 30% marketshare in China, setting off the Chinese AI chip industry. One other company worth mentioning here and quite notably working on a customised AI chip is China's leading facial recognition system provider, Sensetime, which is planning to build another three or four supercomputers to process data feeds for facial recognition from millions of cameras nation-wide.

The AI chip leadership momentum will only continue to intensify in 2019....

Intel, Nvidia, AMD and Qualcomm are not the only one competing to produce an omni product that can infiltrate into gadgets, computers, machines and robots. There are others such as IBM, Amazon, Apple, Facebook, Google, Microsoft, Tesla and many more in the Deep Learning space, all making attempts at the design and potentially a universally accepted standard and product.

Facebook recently sent a strong signal by hiring Google's Head of Chip Development, that the social networking firm is serious about building its own semiconductors, joining the likes of Apple, Google, and Amazon.

Cerebras Systems, a startup still in stealth mode hired a top Intel executive, Dhiraj Mallick as its Vice President of Engineering and Business Development. Mallick served as the VP of architecture and CTO of Intel’s data center group prior to this.

What to expect...?

For now, the market for deep learning chips is overwhelmingly dominated by Nvdia graphics processors (or GPUs), which have also been widely used in games and other graphically-intensive applications.

Startups looking to attack this space, has the opportunity to beat the big chipmakers and create a new generation of hardware that will be omnipresent among any AI devices. Think autonomous vehicle, robotics, drones or even a server within a healthcare organization training models for medical problems.

As new products from companies such as WAWE, Huawei, Cambricon, Graphcore and Habana enters datacenters and selected enterprises this year, we might see a flow of special purpose devices being released into the market for AI and deep learning.

AI chip innovation will also aid researchers to make further breakthroughs in various fileds such as Computer Vision, Conversational AI, Natural Language Processing, Reinforcement Learning, Transfer Learning, and General AI. Eventually some businesses and governments from buyer nations, may start to take advantage of the available AI chip offerings and form their own discreet AI clouds for a variety of high profile projects across the organisation for deeper business differentiation and operational excellence as models train faster and learn in realtime.