Clare Hopping

Hyperscale cloud and data centre operators spent $22 billion on capital expenditure (capex) in the final quarter of 2017 making the total for the year almost $75bn, according to the latest figures from Synergy Research.

Google, Microsoft, Amazon, Apple and Facebook comprise the top five spenders of the 24 major cloud and internet service firms analysed in Q417, according to the data, with growth ‘particularly strong’ at Amazon and Facebook. Alibaba, IBM, Oracle, SAP and Tencent made up the top 10.

A large amount of this spend has gone on building and expanding data centre operations. In December 2016, when the total number of hyperscale data centres passed 300, Synergy predicted this number to surpass 400 by 2018 – a forecast which came in ahead of schedule.

Synergy added capex at Alibaba more than doubled last year – as this publication has regularly reported, the company has driven cloud ambitions with recent focus on European expansion –  while growth of Oracle and SAP, also no slouches in this department, was above average.

“Over the last four years we have seen many companies try and fail to compete with the leading cloud providers. The capex analysis emphasises the biggest reason why those cloud providers are so difficult to challenge,” said John Dinsdale, a chief analyst and research director at Synergy Research.

“Can you afford to pump at least a billion dollars a quarter into your data centre capex budget? If you can’t, then your ability to meaningfully compete with the market leaders is severely limited,” Dinsdale added.

“Of course factors other than capex are at play, but the basic financial table stakes are enormous.”

I am excited to share an interview I recorded with Oracle’s digital, AI, chatbot and mobility expert Suhas Uliyar. In this interview we discuss the meaning of ambient human interfaces, the technology stack that enables chatbots, the power of interfaces that you don’t have to learn, and we learn that algorithms haven’t change that much in 25 years. I learned a lot and hope you will to! Enjoy!

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Lee Bell

Cloud computing in the form we understand today started more than 10 years ago, with the launch of Amazon Web Services (AWS). This was the first commercially viable option for businesses to store data in the cloud rather than on-premise, and acted as a shared service for anyone connecting to the platform.

Early stage cloud computing was certainly more technical than it is now; however, no more than managing a data centre – something that IT departments at the time were well used to. Anyone who was able to successfully start up virtual engines was able to set up a cloud environment.

Cloud started off by offering much more basic services – network, storage and compute. Over the last 10 years, we have seen the volume of data stored in the cloud grow exponentially with businesses frequently dealing with petabytes of data, compared to the gigabytes of yesteryear. With data exploding around us, cloud services have had to learn to be much more efficient.

The cloud of today

As data volumes grew and users grew, cloud providers began to offer more cloud-based value-added services. Analytics and machine learning capabilities that are housed in the cloud are frequently offered alongside other business objective oriented services. As a result, the range of people purchasing cloud services has expanded and is no longer confined to the IT department.

This is because one of the great advantages of cloud and SaaS services is how easy they are to set up and how flexible and scalable they are. Business users without extensive IT knowledge became able to utilise services previously only accessible to the IT department. Of course with this came a rise in  “shadow IT” and while being able to access cloud based business tools were very helpful for achieving business objectives and accessing new services that could not run on legacy IT systems, it did open up businesses to greater data privacy risks.

Today, the IT and business functions increasingly collaborate to implement the most up-to-date, data-driven technologies to solve real business needs, making “shadow IT” less of a problem. By working together, IT and business are able to offer smarter responses to business needs. The business function is better able to define the requirements of their cloud solutions, while the IT department has more flexibility to implement and test different technologies to find the best solution.

An important upside of this is that it allows the IT department to keep a better overview of where data is stored with third party services like Salesforce. To ensure this is implemented throughout the business, the IT department needs to take on an educational role within the organisation – teaching business users about the implications of new data privacy laws and safe and compliant ways to use data within the business. Considering the rapidly approaching GDPR deadline this is good news for organisations as the regulation will mandate much stricter approaches to data protection.

This strategy is being led from the top down, with new data protection officers and C-suite positions like the CDO (chief digital officer) and CTO (chief technology officer) straddling the IT and business function. This is helping redefine the IT department as a department for technological creativity and innovation, rather than simply focusing on solution implementation.

The cloud of tomorrow

With GDPR coming into effect in May 2018, the cloud will need to evolve and adapt. Increasingly, there will be more security services attached to the cloud, as well as greater oversight around what data is stored and where it is. This will be essential for changes like the right to be forgotten, where a person can ask a business to delete all personal data relating to them.

For businesses, this will require significant re-architecting in the cloud environment to increase the ability to analyse and discover data across the storage landscape. The cloud is ready to implement these changes, but it will require a change of mindset within organisations. Cloud usage cannot just be determined on the basis of features and costs, but also on ensuring that the data is stored in a controlled, managed, compliant environment.

In the next five to 10 years, as the volume of data continues to expand exponentially, there will be an increasing need to align this data in terms of format and quality. Organisations will need to be able to pull together multiple data-streams across multiple cloud environments into combined, high-quality insights. This is where an open-source, vendor neutral management layer will become crucial to help organisations bridge the gap between their vast data reserves and the insights offered by machine learning and AI technologies. All of this will contribute to a future where businesses can use data stored in the cloud to provide predictive analytics for the business – such as predicting load requirements for peak shopping days, or market fluctuations to prepare investors.

Dale Walker

• Machine learning patents grew at a 34% compound annual growth rate (CAGR) between 2013 and 2017, the third-fastest growing category of all patents granted
• International Data Corporation (IDC) forecasts that spending on AI and ML will grow from $12bn in 2017 to $57.6bn by 2021
• Deloitte Global predicts the number of machine learning pilots and implementations will double in 2018 compared to 2017, and double again by 2020

These and many other fascinating insights are from the latest series of machine learning market forecasts, market estimates, and projections.

Machine learning’s potential impact across many of the world’s most data-prolific industries continues to fuel venture capital investment, private equity (PE) funding, mergers, and acquisitions all focused on winning the race of Intellectual Property (IP) and patents in this field. One of the fastest growing areas of machine learning IP is the development of custom chipsets. Deloitte Global is predicting up to 800K machine learning chips will be in use across global data centers this year.

Enterprises are increasing their research, investment, and piloting of machine learning programs in 2018. And while the methodologies all vary across the many sources of forecasts, market estimates, and projections, all reflect how machine learning is improving the acuity and insights of companies on how to grow faster and more profitably. Key takeaways from the collection of machine learning market forecasts, market estimates and projections include the following:

Within the business intelligence (BI) & analytics market, data science platforms that support machine learning are predicted to grow at a 13% CAGR through 2021

Data science platforms will outperform the broader BI & analytics market, which is predicted to grow at an 8% CAGR in the same period. Data Science platforms will grow in value from $3B in 2017 to $4.8B in 2021. Source: An Investors’ Guide to Artificial Intelligence, J.P. Morgan. November 27, 2017 (110 pp., PDF, no opt-in).

Machine learning patents grew at a 34% CAGR between 2013 and 2017, the third-fastest growing category of all patents granted

IBM, Microsoft, Google, LinkedIn, Facebook, Intel, and Fujitsu were the seven biggest ML patent producers in 2017. Source: IFI Claims Patent Services (Patent Analytics) 8 Fastest Growing Technologies SlideShare Presentation.

61% of organizations most frequently picked machine learning / artificial intelligence as their company’s most significant data initiative for next year

Of those respondent organizations indicating they actively use Machine Learning (ML) and Artificial Intelligence (AI), 58% percent indicated they ran models in production. Source: 2018 Outlook: Machine Learning and Artificial Intelligence, A Survey of 1,600+ Data Professionals (14 pp., PDF, no opt-in).

Tech market leaders including Amazon, Apple, Google, Tesla, and Microsoft are leading their industry sectors by a wide margin in machine learning (ML) and AI investment

Each is designing ML into future-generation products and using ML and AI to improve customer experiences and improve the efficiency of selling channels. Source: Will You Embrace AI Fast Enough? AT Kearney, January 2018.

SAS, IBM, and SAP lead the predictive analytics and machine learning market based on 23 evaluation criteria applied to 14 vendors by Forrester in 2017

Forrester predicts the Predictive Analytics & Machine Learning (PAML) market will grow at a 21% CAGR through 2021 as evidenced by the increase in client inquiries and purchasing activity they are seeing with clients. Source: Data Science Association, Predictive Analytics & Machine Learning Vendors, 2017 and The Forrester Wave: Predictive Analytics And Machine Learning Solutions, Q1 2017 courtesy of SAP.

Deloitte Global predicts the number of machine learning pilots and implementations will double in 2018 compared to 2017, and double again by 2020

Factors driving the increasing pace of ML pilots include more pervasive support of Application Program Interfaces (APIs), automating data science tasks, reducing the need for training data, accelerating training and greater insight into explaining results. Source: Deloitte Global Predictions 2018 Infographics.

60% of organizations at varying stages of machine learning adoption, with nearly half (45%) saying the technology has led to more extensive data analysis & insights

35% can complete faster data analysis and increased the speed of insight, delivering greater acuity to their organizations. 35% are also finding that machine learning is enhancing their R&D capabilities for next-generation products. Source: Google & MIT Technology Review study: Machine Learning: The New Proving Ground for Competitive Advantage (10 pp., PDF, no opt-in).

McKinsey estimates that total annual external investment in AI was between $8B to $12B in 2016, with machine learning attracting nearly 60% of that investment

McKinsey estimates that total annual external investment in AI was between $8B to $12B in 2016, with machine learning attracting nearly 60% of that investment. Robotics and speech recognition are two of the most popular investment areas. Investors are most favoring machine learning startups due to quickness code-based start-ups have at scaling up to include new features fast. Software-based machine learning startups are preferred over their more cost-intensive machine-based robotics counterparts that often don’t have their software counterparts do. As a result of these factors and more, Corporate M&A is soaring in this area. The following graphic illustrates the distribution of external investments by category from the study. Source: McKinsey Global Institute Study, Artificial Intelligence, The Next Digital Frontier (80 pp., PDF, free, no opt-in).

Deloitte Global is predicting machine learning chips used in data centres will grow from a 100k to 200k run rate in 2016 to 800k this year

At least 25% of these will be Field Programmable Gate Arrays (FPGA) and Application Specific Integrated Circuits (ASICs). Deloitte found the Total Available Market (TAM) for Machine Learning (ML) Accelerator technologies could potentially reach $26bn by 2020. Source: Deloitte Global Predictions 2018.

Amazon is relying on machine learning to improve customer experiences in key areas of their business including product recommendations, substitute product prediction, fraud detection, meta-data validation and knowledge acquisition

For additional details, please see the presentation, Machine Learning At Amazon, Amazon Web Services (47 pp., PDF no opt-in).

​Other statistics

• International Data Corporation (IDC) forecasts that spending on AI and ML will grow from $12B in 2017 to $57.6B by 2021. Source: Machine learning: things are getting intense. Deloitte (6 pp., PDF. No opt-in)
• The global machine learning market is expected to grow from $1.41B in 2017 to $8.81B by 2022, attaining a 44.1% compound annual growth rate (CAGR). Factors contributing to the market’s global rapid growth include new technologies that excel at data aggregation, integration and analysis, and more scalable cloud platforms. Source: Machine Learning Market – Global Forecast to 2022 – Market Overview & Industry Trends.
• Worldwide revenues for cognitive and artificial intelligence (AI) systems will increase from $12.5B in 2017 to more than $46B in 2020. Source: Worldwide Spending on Cognitive and Artificial Intelligence Systems Forecast to Reach $12.5 Billion This Year, According to New IDC Spending Guide.

Sources of market data on machine learning:

2018 Outlook: Machine Learning and Artificial Intelligence, A Survey of 1,600+ Data Professionals. MEMSQL. (14 pp., PDF, no opt-in)

Advice for applying Machine Learning, Andrew Ng, Stanford University. (30 pp., PDF, no opt-in)

An Executive’s Guide to Machine Learning, McKinsey Quarterly. June 2015

An Investors’ Guide to Artificial Intelligence, J.P. Morgan. November 27, 2017 (110 pp., PDF, no opt-in)

Artificial intelligence and machine learning in financial services Market developments and financial stability implications, Financial Stability Board. (45 pp., PDF, no opt-in)

Big Data and AI Strategies Machine Learning and Alternative Data Approach to Investing, J.P. Morgan. (280 pp., PDF. No opt-in).

Google & MIT Technology Review study: Machine Learning: The New Proving Ground for Competitive Advantage (10 pp., PDF, no opt-in).

Hitting the accelerator: the next generation of machine-learning chips, Deloitte. (6 pp., PDF, no opt-in).

How Do Machines Learn? Algorithms are the Key to Machine Learning. Booz Allen Hamilton. (Infographic)

IBM Predicts Demand For Data Scientists Will Soar 28% By 2020, Forbes. May 13, 2017

Machine Learning At Amazon, Amazon Web Services (47 pp., PDF no opt-in).

Machine Learning Evolution (infographic). PwC. April 17, 2017 Machine learning: things are getting intense. Deloitte (6 pp., PDF. No opt-in)

Machine Learning: The Power and Promise Of Computers That Learn By Example. The Royal Society’s Machine Learning Project (128 pp., PDF, no opt-in)

McKinsey Global Institute Study, Artificial Intelligence, The Next Digital Frontier (80 pp., PDF, free, no opt-in)

McKinsey’s State Of Machine Learning And AI, 2017, Forbes, July 9, 2017

Predictions 2017: Artificial Intelligence Will Drive The Insights Revolution. Forrester, November 2, 2016 (9 pp., PDF, no opt-in)

Risks And Rewards: Scenarios around the economic impact of machine learning, The Economist Intelligence Unit. (80 pp., PDF, no opt-in)

Smartening up with Artificial Intelligence (AI) – What’s in it for Germany and its Industrial Sector? Digital/McKinsey & Company. (52 pp., PDF, no opt-in)

So What Is Machine Learning Anyway?  Business Insider. Nov. 23, 2017

The 10 Most Innovative Companies In AI/Machine Learning 2017, Wired

The Business Impact and Use Cases for Artificial Intelligence. Gartner (28 pp., PDF, no opt-in)

The Build-Or-Buy Dilemma In AI, Boston Consulting Group. January 4, 2018.

The Next Generation of Medicine: Artificial Intelligence and Machine Learning, TM Capital (25 pp., PDF, free, opt-in)

The Roadmap to Enterprise AI, Rage Networks Brief based on Gartner research. (17 pp., PDF, no opt-in)

Will You Embrace AI Fast Enough? AT Kearney. January 2018

Clare Hopping

Artificial intelligence (AI) is often touted as the brain behind Industry 4.0. Even the government is backing this emerging technology with its industrial strategy, which aims to “propel Britain to global leadership of the industries of the future including AI and big data”.

Yet research suggests that manufacturers are behind the curve. According to Oneserve, the UK manufacturing industry is failing to invest in AI and creating a potential loss of £151,000 to each individual company, leaving it trailing behind Germany, Austria and the Netherlands.

Manufacturers need to keep up with developments in AI, otherwise they risk being left behind. Failing to prepare for the inevitable will eventually impact their bottom line. They’ll be unable to compete against manufacturers who, with AI at the heart of their digital transformation strategies, will be more productive, agile and prepared for change.

All manufacturers therefore need to embrace disruptive technologies and see them as playing a positive impact on business. It needn’t be as daunting as some think. A first step in transformation is informing staff on their vision and getting commitment from them – C-suite included – as it requires a change in working and often developing new skills. Manufacturers also need to begin researching suitable partners in the technology ecosystem and establish relationships with those that have expertise in the industry. Trusted partnerships will be critical in making AI projects a success.

Crucially, manufacturers also need to keep up with and invest in the cloud, an essential component in AI and other digital projects. It’s therefore vital that AI and cloud companies (and indeed the government) educate manufacturers on the positive impact technology can make. Without this, manufacturers will continue to hold back on investment and be unclear on the benefits of AI.

But is the cloud really that important for AI? Simply put, yes. It’s a technology that is dominant in many businesses today and will continue to be in the future as we enter Industry 4.0. One way that all manufacturers can immediately take advantage of the cloud, with minimal risk, is in their finance department. Not only can it streamline mundane tasks (such as invoicing and accounts payable), removing the administrative burden, and saving businesses thousands of pounds a year, but a cloud-based financial management system can also help CFOs and other senior decision makers view and share important data in real-time from any device and any location.

Similarly, cloud-driven AI can be applied to virtually every manufacturing function. It’s similar to automation in that it can sift through vast amounts of information, automatically crunching data and identifying patterns significantly faster and more accurately than humans, it can streamline processes and improve efficiencies. By automating mundane takes, AI leaves staff to focus on higher-value and strategic activities that could further grow the business.

In finance, for instance, AI and automation can be used as a cognitive computing tool to handle basic accounts payable questions such as when an invoice will be paid. Automating this in a chatbot style response could drastically cut time spent on administrative tasks. It can eliminate paper-based processes – something that many factories still rely on – and ensure manufacturers are operating more efficiently. Decisions can ultimately be made more quickly because manufacturers will have accurate and timely data at their fingertips, allowing greater control.

The bottom line is that it’s important to understand that AI doesn’t demand that manufacturers take a major leap right now. It’s about taking small steps. So, rather than buying into the hype around AI, manufacturers can use more accessible technologies like Robotic Process Automation (RPA), which is based on the notion of AI. They can plan and test RPA software to see the impact it has on its operations and staff, providing an indication of the benefits that AI can bring. That way, they can dip their toes in the water – see what works and what doesn’t – and then expand when they are ready.

Finally, AI and RPA isn’t just for manufacturers. These technologies can streamline virtually every repetitive business process in every market. In fact, we are already seeing industries including agriculture reap the rewards. The possibilities are huge and are continuing at a fast pace. The good news is that both AI and the cloud are yet to reach tipping point, so there’s still plenty of room for businesses to jump on board.

Barry Collins