Barely a day goes past in the tech press without some mention of the importance of digital transformation to businesses; each accompanied by a caveat that nobody really knows what it is. Without engaging further in this debate, what are the absolutes and what really matters?

1. That it’s all about the data. Everything.

How ever we phrase things, the singular, most significant change that technology has brought over the past 100 years is the ability to generate, store, process and transmit inordinate quantities of data. Whatever ‘revolution’ or ‘wave’ we might want to say we are in right now, be it digital, industrial or whatever, there is only really one — the information revolution.

Despite exponential appearances (and resulting perceived impetus for dramatic change), this trend continues with a certain linearity: even as we double the number of pixels on a sensor for example, or transistors on a processor, our abilities increase at a more steady pace. In business terms, the challenges of integration, capacity planning or service level management are the much the same now as they were a decade ago; we are simply working at a higher level of resolution.

2. That technology is enabling us to do new things

This still leaves room for breakthroughs, when technology passes certain thresholds. We saw, for example, the quite sudden demise of the cathode-ray television in favour of LCD screens, or indeed that of film versus digital cameras. What we see as waves are quite often...

Today's leading minds talk AI with host Byron Reese

In this episode, Byron and Hugo discuss consciousness, machine learning and more.

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Today's leading minds talk AI with host Byron Reese

In this episode, Byron and Mark discuss the future of jobs, energy and more.

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Today's leading minds talk AI with host Byron Reese

In this episode, Byron and Adrian discuss intelligence, consciousness, self-driving cars and more.

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The General Data Protection Regulation (GDPR) is a good thing, right? A recent discussion with Fieldfisher lawyer Hazel Grant confirmed that, despite its voluminous and bureaucratic outer appearance, it contains the essence of data protection law as present in the UK for over a decade, combined with the current state of best practice. Given that online privacy knows no borders, it will undoubtedly be better to have a single framework rather than being linked to any single nation or jurisdiction. Word is (for example, from this round-table discussion) that it will make the backbone of privacy law globally — if you’re a multinational, goes the thinking, it makes more sense to implement it once, rather than having different grades of regulation depending on the geography.

Neither is GDPR necessarily the end of the world for ill-prepared (and, potentially, previously in denial) organisations. “The vital thing is to plan, rather than panic,” says Freeform Dynamics’ Bryan Betts, who attended said round-table. “GDPR compliance may not be that onerous, especially if you already handle customer data fairly and transparently.” Even if surveys suggest that 75% of marketing data will be “obsolete”, the 80/20 rule suggests that organisations could do without that bit anyway — ‘keep everything’ is a strategy for the hopeless hoarder, not the business leader.

Yes, GDPR may be expensive to implement, particularly for organisations who have played fast and loose with our privacy in the past, and who now face potential fines with...

The Gigaom founding team is made up of lifetime entrepreneurs, so no surprise, we are super bullish about 2018.

One theme is constant: bright and ambitious entrepreneurs create new opportunities everyday by mapping rapidly advancing innovation to today’s challenges.

The theme is so powerful that is often hard to imagine what new businesses will be born. As investors, it is important to suspend disbelief and dream with the founders about their improved world view. What truth do they see that isn’t fully grasped by the general population? What does success look like if everything goes right?

In 2017, the exponential force of entrepreneurship continued to expand around the globe as “start-up know-how” continues to be democratized by blogs, conferences, and most importantly, by larger exits ($100M+) all over the world, which recycle gains into local start-up ecosystems. Successful local entrepreneurs inspire new founders on what is possible in their area. Additionally, they mentor and invest in these new entrepreneurs and start a powerful reinforcing flywheel.

At Gigaom, we plan to showcase a start-up each month from around the world that is harnessing accelerating technology in new ways to have a big impact on society.

We hope you share our optimism for 2018 and look forward to hearing from you. 

David Hehman, Start-up & VC Editor

To submit your startup to Gigaom’s Monthly Startup Challenge, please use this form.

We’d all love to see technology improve patient care, reduce diagnosis and therapy times and otherwise help us live longer, wouldn’t we? So could distributed ledger technology Blockchain hold the key? In a recent article on PoliticsHome, Member of Parliament John Mann highlighted a couple of areas where Blockchain might offer “transformative potential” to the UK’s National Health Service (NHS):

“By enabling ambulance workers, paramedics, and A&E staff instant access to medical records updated in real-time, medical care could be carefully targeted to a person’s specific needs. The ability to upload results of scans, blood samples and test results and have them accessed by the next practitioner near-instantly, without the risk of error offers the chance to improve survival rates in emergency care and improve care standards across our health service,” he wrote.

While the Rt. Hon. Mr Mann (or his advisors) may be correct in principle, he is falling into an age-old trap by issuing this kind of statement without caveat. While it is a powerful tool (as I noted in my 2018 predictions), it isn’t true that Blockchain enables anything, any more than a chisel enables a sculptor to sculpt. Sure, it could help, but it needs to be in the right hands and used in the right way.

Of course, some might say, this point should be taken as read. If that were the case however, we would not see repeated money being thrown at technology as a singular solution to otherwise...

Whether or not a company is actively involved in developing AI, it’s clear that it’s a powerful force affecting all industries. IDC claims AI was an $8 billion industry in 2016 and will grow to $47 billion in 2020.

However, five years ago there were no cognitive engines as we define them today. Today there are over 5,000 cognitive engines and in the next five years, it is expected there will be well over a million engines.This is a true testament to the fact that the industry is constantly growing and poised to expand even more beyond 2018. Institutions and organizations recognize the necessity of analyzing unstructured data at scale in near real-time.

Unfortunately, the current landscape of artificial intelligence solutions can be expensive, skill-intensive and difficult to implement. Such solutions also tend to be siloed, extremely narrow in their application, and challenged in their ability to deliver real value. In PwC’s Digital IQ survey, only 20% of executives said their organizations had the skills necessary to succeed with AI. As a result, the power of AI has been largely inaccessible to most organizations.

This is all going to be changing as forward-thinking businesses will begin to set aside budget for AI in the coming years. If AI has been on a company’s radar, the good news is that there is still time to learn and strategize, but unfortunately, there will be a huge chasm in business application of early adopters and those who fell behind. But what can the...

Last week, as I took some time out of all things tech, I bookmarked an article from Rick Webb about the failures of the internet, and the culpability of those driving its adoption. It’s a harsh self-indictment of all those driving the digital revolution, specifically the faith paced in the information-rich utopia that would undoubtedly emerge. “I believed that the world would be a better place if everyone had a voice. I believed that the world would be a better place if we all had no secrets. But so far, the evidence points to an escapable conclusion: we were all wrong,” he wrote.

Well, Rick, perhaps I can put your mind at ease. The good news is, you still are: wrong, that is. Even as a smaller number of suddenly powerful people in rapid-growth startups continued to present a utopian vision of the future, the rest of were treating it as it was, and is — a set of tools that can be used for good, ill and everything else in between. There is no “all wrong” just as there never was an “all right”. Even the update to the article, “ “We” is a poor word choice here. Of course there were people — many people — who saw this coming,” is starting from the wrong place, as it still assumes that the options are binary.

Now, I didn’t start this article to give a kicking to some random stranger I have never talked to directly (Hi, Rick). However...

Some technologies continue to exist even if their creators set them aside in favor of something else. A stone wheel remains a stone wheel, regardless of whether you use it. A printing press in a museum still operates precisely as designed, even though it’s been bucked in favor of digital printers. And combustion engines will still run on gasoline, even when the day comes that no one cares to use one anymore.

But this kind of staying power is not the case with the technology that underpins cryptocurrency: blockchain. If people stop caring about a given cryptocurrency, the blockchain that runs it will eventually die out.

How is this possible?

First, let’s review what a blockchain is and how it works. Despite its futuristic-sounding name, it’s actually a simple thing: a digital ledger maintained by a special kind of computer. Traditionally, a ledger is a piece of paper on which changes of ownership are recorded by hand. An ancient ledger might read that I have one sheep and you have three sheep. If either of us buys or sells a sheep, the ledger is updated accordingly. It tracks our account balances, so to speak.

As opposed to sheep, a blockchain records changes in ownership of its “base token.” A blockchain’s base token is also called its “cryptocurrency.” The Bitcoin network’s base token is a bitcoin, the Ethereum network’s base token is an Ether, and the Dash network’s base token is a Dash.

The people who run the specialized computers that execute account balance updates...

Recently I’ve been asking friends, colleagues and clients what they think are the most important unanswered questions in tech. I thank Ian Murphy, who works in the security industry, for the following conundrum:

“Why do companies with little or no real security experience think they know their environment better than anyone else? That is, because it’s ‘their”’ network, they feel best placed to identify attackers (even those with advanced techniques who hide in the normal traffic noise)?” 

It’s a good one. I’ve been working in IT for decades and I remain baffled how we lock up our houses, secure our vehicles, seal away our valuables and yet, in the corporate environment, senior executives still question the need for security expertise. Ignorance, it would appear, is bliss.

While the problem may be technological, I suspect the answer is inherently human. Back in the day, when I was an IT director for a subsidiary of Alcatel, it took a major security incident on my watch to trigger any release of monies from my superiors.

Now, I recognise that I am already looking guilty of transference — wasn’t I the person responsible for securing the network and servers? While this is true, anyone who has worked in this environment know just how complicated it can be to ask for security budget. I know I tried.

And indeed, I remember the feeling of “I told you so” even as I worked with my team to rebuild the previous day’s data sources...

One has to wonder what was going through Barry Boehm’s head when, back in 1986, he formulated what he called the ’Spiral Model’ of software development, which brought the notion of iteration into the process of delivering software to the masses.

He undoubtedly wasn’t the first to employ faster development cycles to solve software problems; however, he was key in presenting such approaches as a viable alternative to exhaustive, long-winded models such as ‘Waterfall’.

General acceptance of ‘fast’ approaches remains a challenge. They are sometimes seen as in some way casual or delivering lower quality, in the same way as people who don’t wear smart clothes might be less disciplined. There’s as much truth to either.

Indeed, Boehm himself released a book about this, in 2004: the title “Balancing Agility and Discipline: A Guide for the Perplexed” says all we need to know about the dilemma, which continues to this day.

Over the decades however, moving faster has been proven over and again as a successful business strategy. Over the past few years, attention has turned from speeding up development, to focusing on the bottlenecks caused by inefficient operations.

Having worked on both sides, I know just how hard it is even to ‘keep the lights on’ in a fast-moving technical environment. At the same time, there remains plenty of room for improvement.

Part of this comes from agreeing an approach, a mindset and a set of formalised controls across development and operations teams. Another, significant...

The use of AI, or artificial intelligence, in the medical field is an
emerging trend that promises exponential advances in the way we
diagnose and treat a multitude of health conditions. Advances in the
application of medical AI technology are occurring at a lightning pace,
with new developments rendering prior solutions obsolete in a matter
of months.

In this article, we’ll review some of the ways that AI technology is
making the healthcare field more efficient, improving the quality of
care, raising ethical concerns, and offering medical practices a
competitive advantage.

History of AI in Healthcare

As early as 1959, the medical research field has been fascinated with
the potential for the application of artificial intelligence. Early
researchers envisioned a machine that could hold a vast amount of
medical knowledge and possess the ability to provide potential
diagnoses. In the early 1980s, the emerging field of Artificial
Intelligence in Medicine (AIM) was urged on by advancements in the
storage and processing power of digital technology. Research
conducted at Rutgers, Stanford, and MIT paved the way for today’s
extensive use of AI in medicine.

Predictive Diagnoses

The use of AI allows medical teams to create diagnoses based on large
data sets. The various medical tests, and the data generated, can be
extremely complicated and extensive. AI can analyze this data in
seconds and observe statistical, as well as causal, relationships in the
data set. These correlations can be difficult or impossible for human
researchers and health professionals to identify. When the patient’s
medical condition is...

Artificial intelligence in the workplace is here to stay. However, as enterprise technologies continue to develop and evolve, we must understand how AI will affect our roles and responsibilities at work.

The unknowns about the impact of AI has led to the fear that this emerging technology could be a substitute for – or entirely eradicate – existing jobs. Depending on which stats you refer to, AI will replace over 40% of jobs by 2030, or that 165 million Americans could be out of work before 2025.

Yet it is not all doom and gloom. Given the rate of new systems, processes, and data that we’re exposed to each day, AI can deliver tangible benefits in learning our skills, habits and behaviors, upending how we use technology. When companies are spending over $3.5 trillion on IT and use an average of 831 cloud services, it’s no surprise that we forget 70% of what we learn in a day, unless we immediately apply that knowledge into
our workflows.

There are four tectonic shifts happening within businesses that are propelling the need for greater personalization and efficiency in how we use technology:

● Employee expectations and behaviors have shifted. Unlike their predecessors, Millennials and Gen Z employees are accustomed to digital technologies. While they’re resourceful and can easily access information, they aren’t necessarily able to retain it. Generally speaking, they expect consumer-level technologies, are highly distracted and change positions often – and thus expect technology to be quick, efficient and intuitive.
● Organizations are undergoing...

We are more comfortable having conversations with machines than ever before. In fact, by 2020, the average person will have more conversations with bots than with their spouse. Twenty-seven percent of consumers weren’t sure if their last customer service interaction was with a human or a chatbot.

To the average person, conversation is simply a more convenient interface, evidenced by messaging services having supplanted social networks in active users. But when it comes to conversational interactions with bots, what exactly do these exchanges mean for machines?

For machines, these conversations are just data. Despite this newfound abundance of ridiculously valuable data, most companies are still just using AI technologies to deflect calls from the contact center.  We have the bigger opportunity to use this data to impact real business decisions across every role, function and department in the enterprise. Yet here we are, about to kick off 2018, and most companies are still leaving the majority of the value of their AI investments on the table.

It’s time to wake up to the data opportunity created by conversational intelligence as a whole.

The value of artificial intelligence has compounding interest

In the world of AI, there is a popular concept called the network effect. This is the concept that a good or service becomes more valuable when more people use it. For conversational intelligence, platforms become...

Predictions are like buses, none for ages and then several come along at once. Also like buses, they are slower than you would like and only take you part of the way. Also like buses, they are brightly coloured and full of chatter that you would rather not have in your morning commute. They are sometimes cold, and may have the remains of somebody else’s take-out happy meal in the corner of the seat. Also like buses, they are an analogy that should not be taken too far, less they lose the point. Like buses.

With this in mind, here’s my technology predictions for 2018. I’ve been very lucky to work across a number of verticals over the past couple of years, including public and private transport, retail, finance, government and healthcare — while I can’t name check every project, I’m nonetheless grateful for the experience and knowledge this has brought, which I feed into the below. I’d also like to thank my podcaster co-host Simon Townsend for allowing me to test many of these ideas.

Finally, one prediction I can’t make is whether this list will cause any feedback or debate — nonetheless, I would welcome any comments you might have, and I will endeavour to address them.

1. GDPR will be a costly, inadequate mess

Don’t get me wrong, GDPR is a really good idea. As a lawyer said to me a couple of weeks ago, it is a combination of the the...

In the era of autonomous vehicles, food delivery by drones, and swiping our way to love, it’s clear that the tedious, time consuming, and often fruitless job recruitment system of old is in need of a tech-makeover. Who better to do it than the all mighty AI.

AI is already affecting the technology powering digital advertising, vehicle connectivity, and financial services. The recruitment industry is also ripe for an AI revolution.

In fact, 15% of HR leaders in 40 countries shared that they believe AI is already impacting the workplace, and an additional 40% believe that AI will significantly influence their decision making, in the coming two to five years. And it should, as implementing AI promises to maximize efficiency, reduce annual business costs, and tackle workplace inequality and discrimination, and more.

AI can process tasks at a scale that most HR teams would struggle with, including quickly and efficiently analyzing thousands of candidates’ applications, saving valuable time and money when searching for talent with the most relevant competencies and experienceNot only does this streamline the recruitment process, it also helps companies hire the most suitable candidates, drastically reducing the chances of hiring an ill-suited employee.  

while maximizing resource utilization enabling the best use of resources.

Hiring the wrong person can be crippling for companies, particularly smaller businesses. A bad hire not only presents a wasted opportunity cost, but equally troubling bi-products such as low productivity, and negative morale....

Today's leading minds talk AI with host Byron Reese

In this episode, Byron and Matt talk about thinking, the Turing test, creativity, Google Translate, job displacement, and education.

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Today's leading minds talk AI with host Byron Reese

In this episode, Byron and Deep talk about the nervous system, AGI, the Turing Test, Watson, Alexa, security, and privacy.

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Today's leading minds talk AI with host Byron Reese

In this episode Byron and Pedro Domingos talk about the master algorithm, machine creativity, and the creation of new jobs in the wake of the AI revolution.

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