Guest-blog: Neil Cattermull – ‘A Guide to Machine Learning’

Neil Cattermull

Because of new computing technologies, machine learning today is not like machine learning of the past. It was born from pattern recognition and the theory that computers can learn without being programmed to perform specific tasks; researchers interested in Artificial Intelligence wanted to see if computers could learn from data.

The iterative aspect of machine learning is important because as models are exposed to new data, they are able to

They learn from previous computations to produce reliable, repeatable decisions and results. It’s a science that’s not new, “but one that has gained fresh momentum”.

While many machine learning algorithms have been around for a long time, the ability to automatically apply complex mathematical calculations to big data “over and over, faster and faster“ is a recent development. Here are a few widely publicized examples of machine learning applications you may be familiar with:

• The heavily hyped, self-driving Google car? The essence of machine learning.
• Online recommendation offers such as those from Amazon and Netflix? Machine learning applications for everyday life.
• Knowing what customers are saying about you on Twitter? Machine learning combined with linguistic rule creation.
• Fraud detection? One of the more obvious, important uses in our world today.

Resurging interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever.
Things like growing volumes and varieties of available data, computational processing that is cheaper and more powerful, and affordable data storage.

All of these things mean it’s possible to quickly and automatically produce models that can analyze bigger, more complex data and deliver faster, more accurate results – even on a very large scale. And by building precise models, an organization has a better chance of identifying profitable opportunities – or avoiding unknown risks.

In the second of a two series blog (“Digital-transformation“), we welcome back Neil Cattermull – Public Speaker and Commercial Director living in London, United Kingdom and a public figure in writing about technology, and entrepreneurship, he is considered a global Industry influencer and authority within the tech scene.

Neil has travelled around the world assisting small to large firms with business models. Ranked as a global business influencer and technical analyst.

Neil has held directorship positions within technology divisions within the financial services market, such as Merrill Lynch, WestLB, Thomson Financial and I have created many small to midsize organisations.

Neil is going to discuss ‘A guide to Machine Learning’.

Thank you Geoff,
Machine learning is one of the most innovative and interesting fields of modern science around today. Something that you probably associate with things such as Watson, Deep Blue, and even the infamous Netflix algorithm.

However, as sparkly as it is, machine learning isn’t exactly something totally new. In fact, the concept and science of machine learning has been around for much longer than you think.

The beginnings of machine learning
Considered to be the father of machine learning, Thomas Bayes’ theorem was pretty much left alone until the rockin 50’s when, in 1950, famed scientist Alan Turing managed to create and develop his imaginatively named ‘Alan Turing’s Learning Machine’.

The machine itself was capable of putting into practice what Thomas Bayes had conceptualised 187 years earlier. This was a huge breakthrough for the field and along with the acceleration of computer development, the next few decades saw a gigantic rise in development of machine learning techniques such as artificial neural networks, and explanation based learning.
These formed the basis of modern systems being managed by artificial intelligence. The latter being arguably the most integral to the development of systems management technology.

Explanation based learning was primarily developed by Gerald Dejong III at the Chicago Centre for Computer Science. He essentially managed to build upon previous methods and develop a new kind of algorithm, enter the “explanation based algorithm!”

Yes, the explanation based learning algorithm was fairly standard in that it created new business rules based on what had happened before. However, what sets this apart as a breakthrough is that Dejong III had managed to create something that would independently be able to disregard older rules once they had become unnecessary.
Explanation based learning was one of the key technologies behind chess playing AI’s such as IBM’s Deep Blue.

A cold AI Winter
However, there was a period during the 70’s when funding was disastrously reduced because people had started thinking that machine learning wasn’t living up to it’s original billing.
This was compounded when Sir James Lighthill released his independent report which stated that the grandiose expectations of what artificial intelligence and machine learning could achieve would never be fulfilled.

This report led to many projects being defunded or closed down. This was incredibly unfortunate timing as the UK was considered a market leader when it came to machine learning. This dark period of time was effectively known as the ‘AI Winter’ and bar a momentary slip in the early 90’s, was the only real time that the possibilities of machine learning were ever really discounted by the scientific community.

Who is pushing the technology forward now?
Machine learning has reached a level now where companies such as DataKinetics have the capability to transform legacy systems into business driven analytics.

DataKinetics are at the forefront of their field and have been entrusted by many blue-chip companies, such as Nissan and Prudential, to streamline and optimize complex technology environments. With the advancements within technology today IT professionals are now capable of achieving so much more due to new innovations in machine learning.
However, this is just the beginning – if funding and interest into machine learning and AI remains consistent, there’s no telling what can be achieved.

Machine learning algorithms that can predict future outcomes, giving us – the humans – to react accordingly.

In essence, the main idea behind machine learning is that it’s essentially where a computer or a system takes a set of data created previously, applies a set of rules to it and provides you with an output that in that is more efficient.

In much the same way, there’s a cycle between the innovators and forefathers of machine learning and with the companies and groups of people that are doing it today.

That’s why companies such as DataKinetics are proud to be associated with such a rich and storied period of human endeavour.
Innovators are equally as important as pioneers, without innovation we have static evolution that does not progress our species further and we are staring at a near constant change in the tech space.

Datakinetics are innovators within technology and have had the foresight to predict the evolution of mainframe, machine learning and analytics with a tech roadmap spanning for over 30 years!

You can contact Neil Cattermull:
– LinkedIn: linkedin.com/in/neilcattermull
– Twitter: @NeilCattermull
– email: Neil.Cattermull@gmail.com

Predictions for the start of 2018!

2017 was definitely one interesting year, and as the Chinese say: ‘We live in interesting times’.

‘2018 will be a year of political turmoil and environmental crisis caused by dramatic and unprecedented weather’, says Craig Hamilton-Parker in a recent blog post.

A man who successfully predicted the unlikely victory of Donald Trump and the UK’s vote to leave the European Union has come up with a new round of prophecies for 2018.

Craig Hamilton-Parker has prophesied there will be a terrorist attack on a British motorway, revolution in North Korea which overthrows Kim Jong-Un’s regime, and a chemical weapons attack by drones on a European city.

On a less morbid note, Mr Parker also predicted Prince Harry would become engaged to Meghan Markle.

Christmas holiday’s are always a period for introspection and once my dreaded cold had calmed down, I started to reflect on some of the most influential push buttons of business and ‘leadership with technology and operating in the new business world came to mind’.

2017 has come to a close and businesses are preparing to enter 2018 with an instant bang.

What do entrepreneurs really expect heading into the new year?

A shift in IT spending: “A significant number of enterprises will begin to invest in a dedicated security operations center as part of the shift away from prevention towards detection and response … Hybrid security offerings combining on-premise and SaaS/Cloud solutions will become the dominant architecture with customers beginning to integrate these offerings starting in 2018.” – Prakash Nagpal, vice president of Infoblox.

The Cloud will fragment into microservices: “In 2018, technology companies are going to ditch the buzzword ‘cloud’ in favor of the next big trend in IT – ‘microservices’. This is where companies will increasingly look to scale by essentially breaking up their IT and thinking smaller and using more SDN and NFV type approaches. Enterprises should also take note fast – moving to smaller applications makes it much easier to scale and decreases risk, while increasing efficiencies.” – Craig Walker, CEO of business communication platform Dialpad

The rise of the sharing economy: “Digitization and the sharing economy will disrupt more industries. Already, retail (Amazon), automotive (Uber and Zipcar), and the server market (Google, Amazon) have been disrupted – and we have had two years without another major industry being disrupted. Given this, financial services and healthcare are ripe for disruption.” – Prakash Nagpal

Banking models will begin a radical shift: “Millennials want to bank wherever they want and whenever they want, which does not align with the traditional banking model. It’s predicted that digital banking will grow to more than 2 billion users by 2020. As a result of this shift, the traditional brick-and-mortar banking solution will be replaced with a technology first-mindset. In essence, your wallet will be your phone.” – Dave Mitchell, president of NYMBUS

Speed is key in modern banking: “The banking channel will strive for speed. Lending, banking services, statement processing and other banking channel players are scrambling to get online and get faster. We expect the scramble to continue as the industry seeks to eliminate middle men – like brokers – and better serve their customers.” – Vernon Tirey, co-founder and CEO of LeaseQ

Mobile banking means more mobile cyberattacks: “All are experiencing a big increase in attacks on their mobile banking and transactions. Expect that to continue. Approximately 80 percent of financial institutions’ customers are doing online banking, 50% are on mobile and that’s growing. More customers equals more opportunity for attacks.” – John Gunn, CMO of VASCO Data Security

Machine learning and Blockchain will grow more prominent: “Two of the most interesting IoT developments to emerge in 2017, with the most potential for innovation, were blockchain and machine learning. They likely won’t go straight to market in the new year – we’ll likely see more proofs of concept instead – but, we have seen some fascinating PoCs already.” – Mike Bell, EVP IoT & Devices at Canonical

Machine learning will become more responsive in customer service: “Machine learning will play a bigger role in sales and customer support in 2018. Lower costs and increased availability of speech analytics tools mean more businesses will record and monitor calls within their contact centers. Instead of simply guiding callers through prompts, speech analytics will help to categorize them and analyze responses in terms of what you say and how you say it. Insights like these will be used to guide agents, in real time, to get the best results from each interaction.” – Chad Hart, head of strategic products at Voxbone

AI implementation will help business capitalize on large troves of data: “Although discussions on the topic of data may not be new, until now most business have been focused on forming teams and building data pipelines, but the data itself has not produced much disruption. With the right people and tools in place, companies can now focus on using data to drive growth. Companies will look to incorporate artificial intelligence (AI) to gain a competitive edge.” – Jennifer Shin, founder and chief data scientist of 8 Path Solutions

IoT cyberattacks will become more common: “There will be an increase of random IoT hacks and attacks because the tools are easy to find and use, and also because of all the unsecured IoT devices – Gartner says there are 8 billion connected things in 2017 and expects 20 billion connected devices by 2020. Anyone can go onto the dark web and start using available malware code, not to mention the readily available services such as hacking, malware- and ransomware-as-a-service, which can all be hired for next to nothing. It’s very easy these days for someone with little knowledge to launch a sophisticated attack, and there’s clear financial incentive – in the last three years, business email compromise alone made $5.3 billion.” – Christian Vezine, CISO at VASCO Data Security

IoT devices will become more secure: “Expect to see at least 2 or 3 large-scale, botnet-style attacks on IoT-related hardware in 2018. To remedy this, the industrial space may pick up a trend from the consumer space, where device updates are downloaded automatically, and give the user little say in the process.” – Mike Bell

Industry will employ more low power wide area networking (LPWAN): “LPWAN technology can be unwired and run for a long time, with minimal power consumption. Its potential applications include heartbeat communications and predictive maintenance for industrial equipment like basement boilers, which can be otherwise difficult to reach … LPWAN provides better penetration and range in hard-to-reach areas, which opens the door for groundbreaking new industrial equipment use cases.” – Mike Bell

Economies are growing. Stock markets are climbing. Employment is healthy. These are all positive signs of the marketplace as a whole.
But the fate of individual companies has never been more uncertain, and the window of opportunity is closing for many companies unprepared or unable to adapt to new market realities.

Many factors are combining to define the fate of companies: Unmet customer expectations are resulting in churn; the lack of digital transformation gains is translating to loss of market share; industry lines that protected some are crumbling; and longstanding, durable business models are failing.

For some, it feels like a burning platform mandating bold action; for others, it will be the quiet but real deterioration of their business.
Customers demand what they demand. And when companies fail to deliver experience by experience or live up to their brand promise, customers will take flight.

Evolving customer expectations will challenge everybody — leaders, followers, and laggards. The across-the-board plateauing of CX (Customer Experience) quality reminds us that customers continuously re-evaluate experiences and reassess loyalties.

Leaders will adapt and, ultimately, thrive. Those slow to change will struggle. And the distance between the two will grow.

2018 will be a year of reckoning for those that have held on too long or tried to bootstrap their way through transforming their business.

Simply put, the distance between customer expectations and the reality on the ground is becoming so great that a slow and gradual transition is no longer possible. Incrementalism may feel good, but it masks the quiet deterioration of the business.

Whether CEOs in these companies start to use their balance sheet wisely, find new leaders, develop aggressive turnaround plans, or do all of the above, they and their leadership teams must aggressively get on track to preserve market share and market standing.

Finally, leaving you with a new year quote and thought by Melody Beattie:

“The new year stands before us, like a chapter in a book, waiting to be written. We can help write that story by setting goals.”