Month: January 2019

15 Jan 2019

Congress Looking to AI and Education for Future Economy

Artificial intelligence is key to the future of the American economy, and investments need to be made now to ensure that the workforce is prepared, said members of Congress during a panel at a Washington Post Live event on Thursday.

When asked if artificial intelligence would put people out of a job, Rep. Pete Olson, R-Texas, cofounder of the Congressional AI Caucus, responded that AI will lead to different, but better-paying jobs. He pointed to examples at companies like IBM and Amazon of retraining and reskilling existing employees to fill AI-related roles.

In retraining local workers in his district, Olson noted that “AI is a big part of that, because it makes that worker better, more viable, more efficient. It drives down costs, drives up productivity, which is just great.”

Rep. Robin Kelly, D-Ill., shared Olson’s sentiments on needing more technology talent. She noted that in the rural, suburban, and urban areas of her district, the demand for skilled workers is a common theme.

Megan Smith, a former Federal CTO and CEO of digital experience agency Shift7, noted that AI could be used for more than economic benefit.

“Why would AI and data science not be for poverty and justice?” she asked. “I believe that the opportunity is about collective genius, and the surface area of participation, that…the more of us who can be included in the conversation, the more likely we are to succeed.”

However, all acknowledged the changes and potential hardships from the rise of AI.

Olson acknowledged that people are likely to experience job transitions and face changes, but he urged people to not be afraid of the change, and embrace it.


14 Jan 2019
Disruptive technology to predict faults on train tracks and in stations

Disruptive technology to predict faults on train tracks and in stations

Train delays could be a thing of the past, thanks to a system that predicts when part of a train track, signaling equipment or other devices at a station are likely to fail. It does this by using thousands of sensors and 3-D modeling that taps into big data.

The system, currently in development, will also allow engineers to use Augmented Reality (AR) via a smartphone or a Head Mounted Display (HMD) to locate failing components or structure faults and read on-screen instructions in real-time to help them with repairs.

The project is a collaboration involving the University of the West of England (UWE Bristol), smart engineering solutions company Costain and engineering technology start-up Enable My Team (EMT), which is the project lead.

A network of Internet of Things (IoT) sensors will initially be installed in 2019 in London Bridge Station, which is to be used as a test site. The sensors will gather data on tracks and station facilities, such as ventilation systems, barriers or lighting before sending it to a software called i-RAMP (IoT-enabled Platform for Rail Assets Monitoring and Predictive Maintenance).

The system will then use Artificial Intelligence (AI) techniques to analyse the data and to predict when a fault is likely to occur and highlights any stress points or component failures on a 3-D virtual model of the station and tracks.

It is set for completion in April 2020, after which it will be trialled with selected customers for up to nine months. Five other train stations in the UK have been approached to serve as testing sites for the technology. The roll-out of the scheme is planned for 2021.

Professor Lukumon Oyedele, Assistant Vice-Chancellor, Digital Innovation and Enterprise, who is the principal investigator on the project at UWE Bristol, said: “Every day in the UK, production is adversely affected by the hundreds of hours lost through train delays, often caused by faulty signal boxes or broken tracks.

The system will enable companies to fix a problem before it even becomes one, and at a time when commuting is not disrupted, all thanks to the IoT sensors in the station and on the track.”

IoT sensors can transmit a whole variety of data including vibration, strain or pressure on a structure, humidity or temperature. Using several such components will enable train companies and station managers to monitor many parts of a train network at the same time.

Read more at:

12 Jan 2019
Ovarian cancer AI can tell how aggressive a woman’s tumour is

Ovarian cancer AI can tell how aggressive a woman’s tumour is

Artificial intelligence is helping researchers spot aggressive forms of ovarian cancer.

Yinyin Yuan and colleagues at the Institute of Cancer Research in London built an AI to look for differences in tumour cell shape. It analysed tissue sample images from 514 women with ovarian cancer and found that misshapen nuclei correspond to a more aggressive form of the disease with a survival rate of 15 per cent over five years. That compares with 53 per cent for the standard form.

Human researchers are very good at looking at cells, but it is hard to quantify differences and the process takes a lot of time – hence the use of AI, says Yuan.

However, the test so far is of limited use, says Kevin Elias at the Dana-Farber Cancer Institute in Boston. “It is one thing to tell me a patient is likely to have a poor outcome, but if you are unable to suggest an alternative treatment, it is not that useful,” he says.

AI is increasingly used in cancer research to sift data for patterns that can help us in various ways, like tracking tumour evolution and improving diagnosis.

Yuan and her team will next use AI to look at cancer that resists chemotherapy, to try to develop more targeted treatments.


10 Jan 2019
Can AI improve mental health at work? Start-up introduces artificial intelligence tech to support employees

Can AI improve mental health at work? Start-up introduces artificial intelligence tech to support employees

A new year means a new start as millions of us make resolutions for the coming 12 months – putting self care at the top of our agenda.

And with the workplace being where we spend most of our time, it is perhaps important to consider how we are looking after ourselves while sitting in front of our desks.

But while we make little changes to our lives to try and live happier, how can technology help us improve our mental health at work?

Startup Humu thinks it has the answer. Run by three ex-Google employees in California, the company uses AI to ‘nudge’ people into being happier at work.

According to the New York Times, Humu’s software monitors data from employee surveys to determine how each worker is doing.

It then communicates with workers via emails and text messages to remind them to take small actions designed to improve their happiness.

Laszlo Block, chief executive of Humu, told NYT: “We want to be the person we hope we can be. But we need to be reminded.

“A nudge can have a powerful impact if correctly deployed on how people behave and on human performance.”

Concerns have been raised about the nature of the nudges and who can give them.

Professor Todd Haugh of Indiana University said the nudges could push employees into behaving in ways that benefited their employers, rather than having their best interests at heart.

However, Jessie Wisdom of Humu dismissed this claim and insisted: “Anybody can do whatever they want.”

She added: “We’re never trying to get people to do things that they don’t actually want to do.”


09 Jan 2019
The meaning of the blockchain

The meaning of the blockchain

THE BLOCKCHAIN, the technology that underlies bitcoin, has yet to live up to the hype surrounding it. Promising blockchain-based projects, such as a land registry in Honduras, have fallen short of expectations. Ersatz securities listings, called “initial coin offerings”, have attracted unfavourable attention from regulators.

Kevin Werbach is a legal scholar at the University of Pennsylvania’s Wharton School of Business and an expert on digital technologies. In the 1990s he was one of the leading thinkers, from his perch at America’s Federal Communications Commission, on how the internet would reshape regulatory policy. In his latest book, “The Blockchain and the New Architecture of Trust” (MIT Press, 2018), Mr Werbach explains how, far from being a radical technology that makes government obsolete, the blockchain relies on the social cohesion, political stability and rule of law that governments often provide.

Kevin Werbach: Blockchains are trust machines, as The Economist recognised in a cover story over three years ago. They’re useful when trusted institutions and intermediaries are problematic, or to overcome a trust gap between transacting organizations. The issue isn’t whether a centralised database could be employed in theory; it’s whether one would be in practice. In contexts like supply-chain management, provenance and trade finance, companies lack a unified view of information because they don’t fully trust their business partners. Blockchain enables what I call “translucent collaboration”: sharing data without giving up control. Whether it’s an improvement over the status quo, however, is highly context-specific.

Read more:

08 Jan 2019
Search... Blockchain's Role in the Enterprise in 2019

Blockchain’s Role in the Enterprise in 2019

Blockchain was invented by Satoshi Nakamoto in 2008 to serve as the public transaction ledger of the cryptocurrency bitcoin. Blockchain has slowly gained traction in the enterprise since its emergence 10 years ago. In fact, late last year we saw digital workplaces using blockchain to share data and collaborate securely.

Blockchain in the Mainstream

Some suggest that blockchain will become mainstream in 2019. Elizabeth White, CEO of White Company, a blockchain based financial services technology firm that operates an exchange/wallet service, a crypto merchant processor and a crypto loadable debit card, agrees that that while 2019 will be the year of mass adoption of blockchain, it will only be for a few key, impactful use cases.

The reality, she said, is that the majority of applications being considered for blockchain simply do not need the distributed, trustless ledger that it offers and can be run faster and better on traditional databases.

White cites the example of supply management, or provenance. A blockchain is not necessary for this use case because there is a narrow group of users needing access to the information and the most important aspect of that information is not the transfer, (which is what blockchain is good for) but rather the input (which blockchain does not solve).

“There are certainly applications of blockchain that touch on supply management as it relates to trade finance,” she said. “The prime use case of blockchain is trustless and automated payments, and leveraging that technology we are building systems for B2B payments that can serve as escrow or conditional payment protocols.”


07 Jan 2019
INSIGHTS Artificial Intelligence Basics For Senior Executives

Artificial Intelligence Basics For Senior Executives

Artificial intelligence has evolved to become one of the most overused and misunderstood terms in business while also offering the potential to be the driving force in business decision-making, automation, and scalability.

Now is the time to develop strategies that set your business up for success for years to come. This article attempts to shed light on the origins, definition, types, business applications, and how senior executives can approach introducing AI to their business.

Defining AI

Artificial intelligence (AI) is a broad term and describes technology’s ability to perform intellectual tasks typically only performed by humans. Technically speaking, a spreadsheet that helps calculate insurance rates based on a range of inputs can be classified as AI.

Applied in a business context AI can describe what happened based on historical data, anticipate what is likely to happen in the future, and provide recommendations on what to do to achieve goals.

The process that made AI the powerful technology it is today is machine learning (ML). It describes the ability of a system to analyse data, identify patterns, and make recommendations by processing data and experiences without explicit programming instructions. ML models adapt and become more accurate over time.

Examples of ML include:

Talent management – organisations identifying which employee traits are correlated to high performance based on CV information and performance review data.

Pricing – ride share services adjusting pricing based on estimated customer propensity to pay a higher price.

Navigation – courier services planning delivery routes based on weather, traffic, and fuel costs.

The latest advancement of ML is deep learning which is a technology that requires even less human guidance and is more accurate than most ML methods. Areas deep learning have helped to evolve include challenging tasks such as image recognition, sound processing, and natural language processing. Google Assistant, for example, is a product of deep learning advancements.

From Zero To Skynet In 200 Years

The fact that AI has evolved into the most disruptive technology since the introduction of the internet is based on the evolution of three major trends.

Big data – The digitisation of our economies and the associated data volumes have been crucial in creating data sets required to effectively train machine learning algorithms. According to Globalwebindex, there are now over four billion people online globally generating vast amounts of data every minute of the day.

Algorithms – Researchers have paved the way for AI by gradually improving algorithms. Theoretical work in the 1800s was brought to life when American scientist Frank Rosenblatt developed the very first machine learning model in 1958.

Computing power and storage – Since Amazon has brought cloud computing and storage capabilities to the mainstream, the costs and ease of access have improved significantly.

These three trends have led us to AI today, a technology so powerful that it already outperforms humans at certain tasks.

How It Works

The machine learning process roughly works the same in each case

  1. Business objective: The business objective is defined and AI might be identified as the way to achieve the objective.
  2. Data preparation: Training data is processed (cleaned and standardised) to make it suitable for the model.
  3. Model draft: A first iteration of the machine learning model is created.
  4. Model training and optimisation: Based on a training data set, the model is fine-tuned to generate better outputs.
  5. Business rules: Business rules are defined to do something with the output of the ML model.
  6. Model deployment: Once the accuracy of the model is satisfactory and the business rules are defined, the model is deployed, which means that “real-world” data inputs (i.e. not training data) can be used to return results.

Rule of thumb here is: more data –> better model –> higher accuracy.

A simplified example of a company going through this process could be a retailer wanting to increase the lifetime value of their online customers.

  1. Business objective: Increase the value of products purchased online per transaction by 25 per cent.
  2. Data preparation: All online and offline purchase data captured via the loyalty program is standardised and transferred into a central database. This data includes customer gender, age, product, product category, and date of purchase.
  3. Model draft: The initial model is created to identify customer segments and the products they are likely to buy together each season.
  4. Model training and optimisation: The initial outputs are compared to the latest real-world data and variable tweaks are needed to make the predictions of the model more accurate.
  5. Business rules: When checking out, each customer should be presented with a last-minute product recommendation that amounts to a minimum transaction value increase by 25 per cent.
  6. Model deployment: The check-out product recommendations are now visible to each website customer and the system will optimise recommendations dynamically based on the latest customer purchase behaviour.

The People Needed To Make It Happen

Larger scale companies who are experienced in ML typically involve a wide range of personnel in projects. Here are some examples. Please note that the job titles and responsibilities might vary greatly across organisations.

Business Analyst – Understands the business needs and determines the outcomes to be achieved.

Data analyst – Defines and sources the data required to solve the business problem.

Data engineer – Establishes the connection between the data sources and the database. She also defines the database structure to ensure efficient access.

Data designer – Defines database structure to ensure efficient access.

Database administrator – Manages the storage facility including performance and security backups.

Data architects – Is across the big picture of data flows and defines the data architecture in collaboration with the data designer and the data engineer.

Data scientist – Uses statistical analysis and data visualisation tools to explore data and creates machine learning models based on findings.

ML Engineer – Deploys the ML model and ensures that IT resources such as processing power and storage are appropriately allocated.

If you have not started introducing AI into your business and you don’t want to hire a whole team from scratch you might want to consider sourcing a vendor with AI capabilities. There is an increasing number of vendors out there and if offshore vendors are an option you’ll be able to find highly qualified talent at a fraction of the cost of western markets.

Making AI Part Of Your Business DNA

Just like any other new technology, ensuring a widespread adoption within your organisation and its culture is a challenge. Considering AI is the most powerful technology known to mankind it has never been as important as it is today to create an effective adoption plan.

The following blueprint for AI adoption can be applied to most businesses.

Stage 1 – Discovery

This is the early stage of AI adoption most business will find themselves in today.

Here it is important to make the most out of your existing resources. Start thinking about the problems you might be able to solve and what data may be required.

Engage some of your existing engineers and ask them to learn about AI and set up an AWS environment to experiment with model templates. Once they feel confident creating basic machine learning models, work with them on designing an MVP. Engage your most loyal customers to test the MVP and capture feedback.

Now you’ll be able to communicate the value AI can bring to the business using the findings of your MVP experiment and align your key stakeholders.

Stage 2 – Engagement 

Engage your key stakeholders to map out how AI can help achieve department objectives. Map existing processes to understand where AI can add value, which employee roles will change and how customer experiences can be improved. The existing prototype may be able to offer improvements already. If new AI capabilities are needed, create a data strategy that prepares the business for future advancements. Data partnerships will help to realise your data strategy faster.

Developing an understanding of how AI relates to other technologies is crucial to ensure future relevance. It may be the most powerful technology of them all but you don’t want to miss out on synergies with others such as IoT, VR, big data, and blockchain.

Change management will be required to take your staff on the AI journey. There will be anxiety around job losses. Sure, some jobs may not be required anymore but AI and general company growth will create new ones. Offering transparency around the use of AI within your organisation and training to the roles likely to be affected by changing job requirements will ensure a smooth transition to the next stage.

Read more:

06 Jan 2019
How project managers can harness the power of disruptive technologies

How project managers can harness the power of disruptive technologies

Technology is changing the way companies operate. In the modern business environment, the development of digital-age skills is important to project success

Disruptive technologies are reshaping industries and markets in unprecedented ways and at extraordinary speeds. The organisations that succeed in this environment are those that can adapt rapidly to new opportunities and challenges. Rather than only seeing threats, they recognise that disruptive technologies can, in fact, give them a competitive advantage.

We are clearly in a period of profound change for the workplace – and all strategic change in an organisation is delivered through projects and programmes. Project management, therefore, holds enormous value in helping organisations to leverage emerging technologies.

Artificial intelligence, for example, holds the promise of automating more routine aspects of work, such as tasks related to scheduling. This allows project managers more capacity to play greater strategic roles in their organisations.

Digital-age skills
Recent research clearly indicates that organisations can do more to strengthen their project management capabilities. In fact, according to the Project Management Institute’s (PMI’s) 2018 Pulse of the Profession global survey, organisations waste an average of €99m for every €1bn invested in projects.

Organisations with cultures that embrace change are better positioned to succeed in a fast-paced and disruptive business environment

Too much money is being wasted on poor project performance for a few key reasons. We have found that organisations often don’t bridge the gap between strategy design and delivery. We have also noted that many executives fail to recognise that strategy is delivered through projects. As such, the importance of project management as the driver of an organisation’s strategy is not fully recognised in most cases.

Further, a recent Brightline Initiative study, conducted by the Economist Intelligence Unit, showed that 59 percent of senior executives admitted their organisations struggle to bridge the strategy-implementation gap. As a result, only one in 10 organisations successfully reach all of their strategic goals. So, what can organisations do to more effectively execute their ideas and take advantage of emerging technologies?

Our research points to a few recommendations. PMI recently sought to understand how forward-thinking organisations successfully leverage disruptive technologies and manage their impact. In our most recent report, The Project Manager of the Future: Developing Digital-Age Project Management Skills to Thrive in Disruptive Times, we discovered that the successful management of disruptive technologies requires a ‘digital skill set’.

Read more:

05 Jan 2019

Artificial Intelligence And The End Of Government

Even as artificial intelligence (AI) is forecast to exceed human capabilities across a range of industries it is also predicted to augment human labor. In finance, AI is already helping financial advisors augment financial planning while enhancing investment strategy. And in medicine, AI diagnostics systems have proven to be far more accurate than doctors in diagnosing heart disease and cancerous growths. In fact, McKinsey lists some 400 use cases representing $6 trillion in value across 19 industries in which AI will augment human work.

But what about government? What will the impact of AI be on the nature of government?

Waking Government to AI

Not surprisingly much of the public sector has already begun experimenting with AI-driven technologies. At the federal level many agencies are beginning to deploy AI-powered interfaces for customer service, alongside an expanding use of software to update legacy-systemsand automate simple tasks. Growing investments in infrastructure planning, legal adjudication, fraud detection, and citizen response systemsrepresent the first phase in the ongoing digitization of government.

Notwithstanding these investments however, government remains far behind the private sector in deploying and integrating AI. As Silicon Valley’s Tim O’Reilly has suggested, augmenting government through AI is critical to modernizing the public sector. AI-based applications could potentially reduce backlogs and free workers from mundane tasks while cutting costs. According to Deloitte, documenting and recording information alone consumes a half-billion staff hours each year, at a cost of more than $16 billion in wages. Add to this an additional $15 billion in the procuring and processing of information and the value of AI in transforming government bureaucracy becomes clear.

Read more:

03 Jan 2019

Retrofitting AI – key adoption issues in the enterprise 2019-2020

AI technology has moved beyond the hype phase, but short-term adoption of AI in organizations will primarily come through third-party software and relatively straightforward application of Machine Learning, even though many organizations are not yet ready for the latter.

The 2018 AI hype machine was as close to jumping the shark as anything I’ve seen over more than 30 years understanding this field of technology innovation.

Machine Learning holds the greatest promise yet much needs to happen before firms see a genuine business value stream, Even so, there are excellent opportunities for organizations retrofitting AI functions into their own applications to boost speed, accuracy, and productivity.

Caution: AI cuts to the core of human contribution and will need vigilant leadership to prevent disorganization, distortion and dysfunction. It is just as likely that human experts in select fields such as finance, underwriting, claims processing and credit, for example, will be co-opted by AI adoption as those performing manual processes.

Artificial intelligence (AI) is old technology, with new implementations. However, the advent of increasingly parallel programming models and unprecedentedly scalable hardware, coupled with the opportunity to pursue significant new business value served to make AI 2019 tech’s glittering fashion statement. As executives consider adding AI to their business system portfolio over the next 24 months, they must understand the following:

  • Not everything called AI is real. Psychologists and neuroscientists are still trying to understand what human intelligence is, so “intelligence” in the context of “artificial” and “human” is the same word to describe two different things. Think Paris, France and Paris, Texas. Distinguishing between core AI disciplines and technologies and AI applications that are built from those technologies is important to keep track of AI investments and expected business outcomes (see Figure 1).
  • In 2019, AI can stand for “additive intelligence.” Organizations will find that their existing applications can be enhanced with the application of AI “wrappers,” particularly replacing manual data ingestion, human expert forecasting, and data discovery. It is becoming easier for in-house developers to use AI technology, especially since Amazon AWS, IBM Watson and Microsoft Azure, among others, provide useful API’s for AI algorithms. However, enterprise software providers have far more resources to implement AI capabilities and most AI will be added to business systems through software packages.
  • AI can lead to organizational distortion and dysfunction. AI implementation has a direct effect on the nature of work in organizations. Adjusting to this is never simple. Employees see AI coming and they will push back, either purposely or not.