It’s the end of April, and that can mean only one thing: Michael Dell is getting ready to emerge, bear-like, from his Winter slumber and ravenously tear into something; in this case, insufficiently transformed data centres.

For the next week, Las Vegas’ Sands expo centre will be swarming with Dell’s customers, partners and technologists, all eager to hear what the company has in store over the next 12 months.

The answer, I suspect, will not be a surprise. Dell has been beating the ‘digital transformation’ drum for several years, and it shows no signs of letting up.

As such, we can expect to hear all about the company’s favourite talking-points (sing along if you know the worlds), including the importance of multi-cloud architectures, the growing role of data analytics in business and how software-defined storage and networking can unlock ‘the data centre of the future’. All important topics, to be sure, but nothing that we haven’t heard Dell talking about at its previous conferences.

In the years since the arrival of cloud to the enterprise market, Dell has pivoted rather impressively to being a full-stack provider, broadening its focus from on-premise infrastructure to hybrid cloud, edge computing and IoT in a way that its rivals haven’t been able to match quite as effectively. Realistically, there are very few parts of the modern data centre that Dell doesn’t touch in some way, and the company will be making full use of this position.

Edge computing and IoT, in particular, will almost certainly play a major role in this year’s conference; both areas are a key part of the so-called ‘4th industrial revolution’, and are supported by Dell’s product portfolio.

Analytics will likely be a major theme too – data-crunching is increasingly vital for companies, and by happy coincidence, the high performance and low-latency storage of Dell’s equipment makes it well-suited to this task.

Expect to hear the phrase ‘multi-cloud’ a lot, as well. Dell likes to emphasise how well the full suite of Dell Technologies brands (primarily VMware, Dell EMC and Pivotal) lend themselves to mixed estates, lest anyone think of it solely as a tin shop.

What all of these areas mean for customers, in practical terms, is another matter. We’d be surprised to see any major announcements from VMware – they’re usually saved for VMware’s own conference later in the year – but CEO Pat Gelsinger is all but certain to deliver his usual keynote. This will probably be where 5G, IoT and Edge receive the most airtime.

VMware aside, any cloud announcements are likely to come from Pivotal Cloud Foundry, which is one of Dell Technologies’ major entry-points to the cloud market.

The meat of Dell’s announcements is most likely to focus on new hardware. We’re a little too early in the lifespan of Dell’s latest range of 14G PowerEdge servers to expect a whole new generation. But what we could well see are some newer, more powerful products. Intel has recently announced a swathe of new Xeon server processors, so we’re expecting Dell to show off some fancy new iron that actually makes use of them.

These servers will, no doubt, be touted as an ideal way to accelerate your machine learning and/or data analytics deployments; a position that neatly dovetails with Dell’s preferred messaging.

Equally, don’t be shocked to see Dell unveiling some new servers running on AMD’s EPYC architecture. It’s less likely than new Xeon-powered models – Intel is a major Dell partner with a lot of behind-the-scenes pull, so the company will want to avoid antagonising Intel – but at the same time, Dell’s run by smart people. It’s no secret that Intel’s 10nm development has hit a bit of a brick wall, while AMD has sailed merrily past it onto the 7nm process node.

The results speak for themselves, too: Dell EMC servers fitted with AMD’s EPYC processors can match the performance of Xeon-based equivalents for a considerably lower price point, and that can’t have gone unnoticed. How much attention AMD gets (both on stage and in the halls) should give a good indication of whether the tide is starting to turn in its favour.

Long story short, keep an eye out for more AMD servers than usual – although they won’t be sporting the company’s newest Rome architecture, as it’s still too early for production servers to be ready.

We wouldn’t discount the possibility of Dell launching some new storage or networking hardware, either. We’re not betting the farm on this as it’s usually not a ‘sexy’ enough area to get much attention at the company’s major league show, not to mention that both portfolios got a full refresh not long after the close of the EMC acquisition. That being said, both storage and networking are key areas of data centre transformation, and Dell has been impressing in both categories recently.

Additionally, it’s worth noting that this may well be a more cautious show than we’ve seen in recent years, as it’s the first annual conference since Michael Dell took the company back onto the public market last year. This means that, for the first time since 2013, he’s once again answerable to shareholders. Dell is riding high on the successful execution of its roadmap, but tech investors are a notoriously skittish bunch, so the pressure will be focussed on not spooking them with any controversial announcements.

Thematically-speaking, then, this year’s Dell World is set to be more of the same. Regular attendees probably aren’t going to find themselves surprised by the company’s agenda, and although we may have some excitement on hand in the form of new hardware releases, the looming spectre of public investors makes any big shocks fairly unlikely.

Still, for customers and partners, it’s an opportunity to get a closer look at the latest products and services rolling out of Dell’s development facilities; at the end of the day, that’s what it’s really all about.

If there’s one resource the world isn’t going to run out of anytime soon it’s data. International analyst firm IDC estimates the ‘Global Datasphere’ – or the total amount of data stored on computers across the world – will grow from 33 zettabytes in 2018 to 175 zettabytes in 2025. Or to put that in a more relatable form, 175 billion of those terabyte hard disks you might find inside one of today’s PCs.

That data pool is an enormous resource, but one that’s far too big for humans to exploit. Instead, we’re going to need to rely on deep learning to make sense of all that data and discover links we don’t know even exist yet. The applications of deep learning are, according to Intel’s AI Technical Solution Specialist, Walter Riviera, “limitless”.

“The coolest application for deep learning is yet to be invented,” he says.

So, what is deep learning and why is it so powerful?

Teaching the brain

Deep learning is a subset of machine learning and artificial intelligence. It is specifically concerned with neural networks – computer systems that are designed to mimic the behaviour of the human brain.

In the same way that our brains make decisions based on multiple sources of ‘data’ – i.e. sight, touch, memory – deep learning also relies on multiple layers of data. A neural network is comprised of layers of “virtual or digital neurons,” says Riviera. “The more layers you have, the deeper you go, the cleverer the algorithm.”

There are two key steps in deep learning: training and inference. The first is teaching that virtual brain to do something, the second is deploying that brain to do what it’s supposed to do. Riviera says the process is akin to playing a guitar. When you pick up a guitar, you normally have to tune the strings. So you play a chord and see if it matches the sound of the chord you know to be correct. “Unconsciously, you match the emitted sound with the expected one,” he says. “Somehow you’re measuring the error – the difference between the two.”

If the two chords don’t match, you twiddle the tuning pegs and strum the chord again, repeating the process until the sound from the guitar matches the one in your head. “It’s an iterative process and after a while you can basically drop the guitar, because that’s ready to go,” says Riviera. “What song can you play? Whatever, because it’s good to go.”

In other words, once you’ve trained a neural network to work out what’s right and wrong, it can be used to solve problems that it doesn’t already know the answer to. “In the training phase of a neural network, we provide data with the right answer… because we know what is the expected sound. We allow the neural network to play with that data until we are happy with the expected answer,” says Riviera.

“Once we’re ready to go, because we think the guitar is playing well, so the neural network is actually giving the expected answer or the error is very close to zero, it’s time to take that brain and put it in a camera, or to take decisions in a bank system to tell us that it’s a fraud behaviour.”
Deep learning as a concept isn’t new – indeed, the idea has been around for 40 years. What makes it so exciting now is that we finally have all the pieces in place to unlock its potential.

“We had the theory and the research papers, we had all the concepts, but we were missing two important components, which were the data to learn from and the compute power,” says Riviera. “Today, we have all of these three components – theory, data and infrastructures – but what we’re missing is the fourth pillar, which is creativity. We still don’t know what we can and can’t achieve with deep learning.”

Deeper learning

That’s not to say that deep learning isn’t already being put to amazingly good use.

Any regular commuter will know the sheer fist-thumping frustration of delays and cancelled trains. However, Intel technology is being used to power Resonate’s Luminate platform, which helps one British train company better manage more than 2,000 journeys per day.

Small, Intel-powered gateways are placed on the trackside, monitoring the movements of trains across the network. That is married with other critical data, such as timetables, temporary speed restrictions and logs of any faults across the network. By combining all this data and learning from past behaviour, Luminate can forecast where problems might occur on the network and allow managers to simulate revised schedules without disrupting live rail passengers. The system can also make automatic adjustments to short-term schedules, moving trains to where they are most needed.

The results have been startling. On-time arrivals have increased by 9% since the adoption of the system, with 92% of trains now running to schedule.

Perhaps just as annoying as delayed trains is arriving at the supermarket to find the product you went there for is out of stock. Once again, Intel’s deep learning technology is being used to avert this costly situation for supermarkets.

The Intel-powered Vispera ShelfSight system has cameras mounted in stores, keeping an eye on the supermarket shelves. Deep-learning algorithms are used to train the system to identify individual products and to spot empty spaces on the shelves, or even products accidentally placed in the wrong areas by staff.

Staff are alerted to shortages using mobile devices, so that shelves can be quickly restocked and lost sales are kept to a minimum. And because all that data is fed back to the cloud, sales models can be adjusted and the chances of future shortages of in-demand products are reduced.

Only the start

Yet, as Riviera said earlier, these applications of deep learning are really only the start. He relays the story of the Italian start-up that is using deep learning to create a system where drones carry human organs from hospital to hospital, eliminating the huge disadvantages of helicopters (too costly) and ambulances (too slow) when it comes to life-critical transplants.

It’s not the only life-saving application he can see for the technology, either. “I’d like to see deep learning building an autonomous system – robots – that can go and collect plastic from the oceans,” he says. “We do have that capability, it’s just about enabling it and developing it.”

“The best [use for deep learning] is yet to be invented,” he concludes.

Discover more about data innovations at Intel.co.uk

Amazon has unveiled record high profits, obtaining more than double the levels investors were predicting, with much of its gains coming from the AWS division.

It will come as no surprise to regular readers of Cloud Pro that the cloud arm of Amazon is reeling in the most cash of all its parts, achieving $7.7 billion in revenue ($2.2 billion profit), which represents a 41% year-on-year growth.

However, its cloud revenues of $7.7 billion only represented 13% of the company’s total revenues over the first quarter of the year, but taking almost half of its total profits – demonstrating just how profitable the division is for the company’s bottom line.

There were some big cloud achievements for the firm over the quarter, including the launch of the AWS Asia Pacific (Hong Kong) Region, and announcing plans for the AWS Asia Pacific (Jakarta) Region, the launch of Amazon S3 Glacier Deep Archive, Concurrency Scaling for Amazon Redshift and general availability of Amazon EFS Infrequent Access.

Plus new business from some of the world’s biggest companies including Volkswagen, Ford, Lyft and Gogo had a positive impact on its cloud business.

Revenues for the entire company hit a Wall Street-beating $59.7 billion, although spending on technology such as AI and smart homes has also increased, meaning profits weren’t as high as they could have been and growth is likely to slow in the future.

It’s also spending a lot on developing its own cloud infrastructure, again making a dent in profits in the short term, and its planned one-day Prime shipping rollout is also likely to cost a fair whack in terms of logistics.

Despite being a leader in the server chip market, Intel’s revenue from its data-centric business has dropped by 5%, according to the company’s 2019 first quarter financial results.

While the chip maker reported a 5% hike in cloud revenues in its Data Centre Group, revenues for the arm dropped by 6%. What’s more, overall revenue from its data-related businesses raked in $4.9 billion, which is down from $5.2 billion in the same quarter for 2018.

One of the areas contributing to this decline was the 4% drop in average selling prices and the 8% drop in the volume of units Intel shifted for its data centre platform, which is a continuation of the rather sharp slowdown Intel’s server equipment sales encountered last year. Historically, this has been a booming business for the chip maker.

Yet, while Intel’s data-centric business isn’t as healthy as it would like, revenue from selling desktop and laptop processors rose by 4% to generate $8.6 billion for the company. This rise came in spite of static PC shipments and problems Intel encountered around chip shortages.

Intel’s chief executive Robert Swan also noted that next-generation laptop processors built on the 10nm process node – thereby increasing the number of transistors in a processor to improve power and efficiency – will arrive this year.

“Our confidence in 10nm is also improving,” Swan said in an earnings call transcribed by Seeking Alpha. “In addition to the manufacturing velocity improvement I described earlier, we expect to qualify our first volume 10nm product, Ice Lake, this quarter and are increasing our 10nm volume goals for the year.”

This next-generation of processor could help keep Intel’s PC-centric business ticking along. Swan noted 10-nanometre chips will be rapidly brought from the PC business and into Intel’s data centre chips.

«Historically there has been a 12-18 month gap between client chips and servers. On 10nm that will be much shorter, all we’ll say is it will be a fast follow, some time in 2020, and earlier rather than later,” Swan added.

With a decline in demand from data centre customers and the investment Intel is making into getting its 10nm fabrication ready for the production of chips at volume, the company has reduced its earnings forecast for the full 2019 fiscal year to $69.0 billion from a previously estimated $71.05 billion.

Nevertheless, the determined push to 10nm chips in both the PC and data centre space world could see Intel continue to do well in the PC market while also giving its data-centric business a shot in the arm come some point in the future, most likely 2020.

Whenever artificial intelligence, aka AI, comes up in conversation, the usual image that springs to most people’s minds is a threatening killer robot along the lines of Terminator that has nothing but murderous intentions towards humanity.

But these days the AI acronym is being liberally sprinkled far and wide, often referring to things that stretch well beyond its primary meaning. In this feature, we look at some of the common myths and misconceptions, compared to the real scientific situation in each case.

1. AI will create a malevolent Skynet-style system that will destroy humanity

Let’s look at the most popular myth first – the scary robot elephant in the room. Terminator is the most well-known example, but it is a recurring sci-fi theme from 2001: A Space Odyssey to the latest season of Star Trek: Discovery. On the one hand, technology has been automating the delivery of ordnance for decades, with in-missile video footage from the 1991 Gulf War just one watershed moment in a process towards greater autonomy that dates back to the German V1 and V2 rockets of World War II. The US army has been deploying Unmanned Air Vehicles (UAVs) for decades, and now has around 10,000 of them in regular use. But all of these still have human operators for key functions. The Defence Advanced Research Projects Agency (DARPA) has been awarding grants for the development of UAVs that can navigate themselves indoors. But as the RAND corporation points out, very few countries use armed drones just yet, and there is much controversy about their central value in warfare compared to conventional weapons systems. So even if fully autonomous fighting machines are developed, there are still many hurdles before they are deployed without human oversight, let alone take over the world.

2. AI systems and robots will eventually replace all jobs, making most people redundant

According to a report published by the UK’s Department of Work and Pensions, 8,820,545 jobs could be wiped out by 2030 because of AI, particularly in the retail sector. Aside from the strangely specific number of job losses predicted, it’s worth noting that it won’t just be menial labour that gets replaced. Complex intellectual activities are already being replicated by expert systems, such as legal and medical advice. AI has been making inroads into healthcare to allow earlier diagnosis without the need for consultation with specialists, who are always at a premium. You can even put your job title into the Will Robots Take My Job website to see how likely you are to be replaced by AI. In reality, though, similar arguments have been made since the agrarian and industrial revolutions. On the one hand, many jobs will be automated by AI, but on the other, people can retrain, or young people educated in a different direction for the new jobs that are emerging – potentially designing and building those AI-powered robots.

3. Siri, Google Home, Cortana and Alexa are AI

Voice-activated speakers have been a Christmas hit for the last couple of years, and more people are getting used to giving the smartphones verbal commands, too. These are undeniably clever, convenient systems (when they work…), but in reality, they are just advanced natural language processing (NLP) recognition algorithms akin to dictation software like Dragon Naturally Speaking. There is no original thought going on, just a lot of pre-programmed responses to verbal commands.

4. AI is a computer version of the human brain

We now get to the main underlying myth of AI – that computers model the human brain. This could be the subject of multiple PhD dissertations, but in a nutshell (and just for starters), computers are still based on the Von Neuman machine model of the mid-1940s. This reads data from memory, operates on it, and writes the result back to memory. This is not how brains work. Even multi-core processors, or HPC datacentres full of them, are still much more serial in their operation than a brain, which in contrast has a slow frequency (around 200Hz compared to multiple Gigahertz) but is massively parallel. Not just massively parallel, but inputs and outputs are connected in complicated feedback loops, with workings that we still don’t understand completely yet. This isn’t to say that computer AI isn’t amazingly useful, or that we won’t ever fully understand the human brain. But current AI is at best a very rough simulation, not even close to a digital facsimile of the cerebrum of homo sapiens.

5. AI systems can learn for themselves

Another two-letter acronym often found alongside AI is ML (Machine Learning). The common myth is that ML is a fully autonomous process, which will potentially lead to AI that transcends human intelligence and eventually decides to get rid of us (see 1 above). However, ML still intrinsically involves teaching by humans. Every AI system needs to be fed source material chosen by people, and its outputs adjusted by human experts until they work. This is precisely the process that Google’s self-driving car system is going through right now, and this won’t stop even when it gets the green light as a commercially available system in new vehicles.

6. AI systems will be much more impartial than human beings

As a result of AI’s ML being fed by humans, there’s no reason to believe that it will be any more impervious to prejudices than the humans that taught it. Early facial recognition systems had trouble identifying ethnicities, and the Tay Twitter bot was rapidly turned into a rabid racist by the tone of social media conversations. On the other hand, an AI that has been trained to be as impartial as the best human examples will consistently be better in this respect than the worst humans, which is where there is clear value for the legal and medical professions, amongst others.

7. AI will soon be smarter than human beings, or never will

Because AI runs on computers that are not an exact replica of the human brain (see four above), this is a bit of a trick question. On the one hand, there is no sign that a general AI will transcend overall human intelligence anytime soon, because we still don’t know exactly what the latter is. But on the other hand, much more narrow AI has been beating humans for some years now, such as defeating the best human chess player, and surpassing the best players of Jeopardy!. In 2004, none of the vehicles in the DARPA Grand Challenges completed the course, but in 2005, five did, and now we have self-driving cars being tested on public roads. So AIs will likely be smarter than humans in many key areas (and already are in some), but may never be in a general, overall sense – whatever that even means.

8. The technological singularity is approaching, or will never happen

Related to seven above, there is a theory, originally presented by maths and computer science professor Vernor Vinge, that the development of artificial superintelligence will arrive around 2030 and then AI will upgrade itself beyond human understanding and the world will change unrecognisably. This has been dubbed “the singularity”. For reasons already discussed above, the date already looks massively optimistic before we even get into the details, due to our lack of understanding of the human brain. But, on the other hand, the singularity doesn’t necessarily have to involve completely human intelligence. We could be creating something that isn’t human, just loosely based on us. Nevertheless, this idea still gives computers the ability to have their own intentions, which somehow emerge spontaneously from the advanced technology. Right now, no AI can do anything other than what humans told it to do, even if your flaky desktop PC might sometimes make you believe otherwise.

9. AI is just sci-fi and nothing that should concern business

Just as AI can create new jobs as well as replace old ones, it’s a topic that should be central to all businesses that intend to survive and grow over the coming decades, rather than ignored. On the one hand, the scary sci-fi scenarios of human replacement are very distant if ever likely to happen at all. But, on the other hand, there are real opportunities to enhance business services and consumer interaction via the more limited systems that have been shown to be effective already. Business intelligence data analytics, forward resource planning, and automated customer service are just the beginning. Companies that embrace AI, whilst being realistic about its limitations, will be the ones that prosper.

Discover more about the AI solutions powered by Intel here

Slack has added some new features to its collaboration platform which aim to embrace the power of email, the very tool it aimed to kill off over five years ago.

Instead of having the two services operate alongside one another, now those in your organisation who aren’t on Slack, or have just started and haven’t yet received credentials, can still benefit from its collaboration features.

Directly addressing an individual in Slack via it’s ‘@-mention’ feature can now be utilised within email, with notifications appearing in the employee’s inbox if they are not on the platform or logged in.

Replies sent from an employee’s inbox will beam straight back to the relevant channel just as if the interaction was taking place on just the one platform.

Admins will need to tweak their company’s account to allow outside users to communicate with those inside the organisation in this way, but it’s a step closer to being a more unified collaboration tool.

This supports Slack’s existing Outlook and Gmail functionality, which allows users to forward emails into a channel where members can view and discuss the content and plan responses from inside Slack.

Another interesting announcement, made at the company’s Frontiers conference in San Franciso, relates to its ‘Workflow Builder’ tool which will enable any user within Slack to build apps for routine functions without coding knowledge.

The tool, which is launching later this year, will be capable of automating functions, such as completing and filing a benefits request form to HR or sending messages to help new starters find the right channels to join, saving other workers from sacrificing time to give a platform tutorial.

If this sounds familiar then you’d be right. Slack announced back in February two new toolkits that would also allow non-coders to build apps within Slack, however Workflow Builder appears to be geared towards routine automation rather than the more technical backend functions of the platform.

Slack’s integration with Outlook and Google Calendar is also becoming stronger as any status you set within Calendar will be automatically synced to Slack, such as being away from the office for an event or when you have a meeting booked in.

As many business meetings tend to be virtual nowadays, integration with calendars will allow other users to see who your meeting is with and provide joining options directly within Slack thanks to partnerships with Hangouts, Zooma and Webex.

There is also a change coming to Slack’s search function which, although fast and expansive, isn’t always the most intuitive or organised. Slack aims to address this by adding new features to make it easier to view unread messages quicker, allow faster navigation between channels to find the relevant person, and better functionality when sifting through channel archives. These features will be available in the coming weeks.

Slack’s five-year slog of a battle with email has proved fruitless; email still exists and seems like it’s here to stay. Google has invested into it to a greater extent recently despite the wide adoption of the platform which depends on the virality of its freemium model.

Manufacturing, lumber, distribution; these kinds of industries have been around for hundreds of years, and those who run companies in these sectors are used to doing things a certain way. Change is slow and, if business is good, can be seen as more of a risk than an opportunity.

This is the challenge that enterprise resource planning (ERP) firm Epicor aimed to tackle as it entered into its annual Insights conference, hosted this year at Mandalay Bay in Las Vegas. And, if the company’s executives are to be believed, the migration of its flagship product line-up to the public cloud is certainly the future.

This could lead to new technological possibilities, their message says, with the biggest announcements last week centring on enhanced ERP functionality prefaced by a stronger partnership with Microsoft’s Azure platform.

The most eye-catching of these was the Epicor Virtual Agent (EVA), an AI-powered digital assistant that the company’s chief product and technology officer Himanshu Pulsale insisted wasn’t «just a chatbot». Epicor’s big bet on the cloud was also met with a commitment to something it calls the ‘connected enterprise’, or in other words letting Internet of Things (IoT) devices loose across the factory floor. But this direction of travel hasn’t been embraced as warmly among a significant chunk of Epicor customers.

Just a few years ago the company was in rocky waters under its previous leadership, adopting a defensive mentality with regards to its customers, and almost apologising when they integrated any new tech into their products. But now the company is banging the drum for digital transformation and, in particular, public cloud migration.

Epicor has embarked on a decisive drive to communicate these benefits to a sceptical customer base, a small proportion of which have no interest in pursuing cloud technology whatsoever.

The software giant places the number of its customers still using Epicor ERP 9, released almost a decade ago, and similarly outdated software at roughly under 500 of 3,000 in the manufacturing space. Moreover, questions often asked throughout the conference ran along the lines of «do I need to be on cloud to use this feature or can I get it with on-prem as well?».

One of the deepest concerns among a more older generation of customers, as Epicor sees it, is that they’re being forced into using the cloud against their wishes. They are, in fact, quite comfortable with how their infrastructure sits right now. The biggest worry for Epicor is they’ll sit on whatever system they have for as long as possible, and then look at rival options – instead of upgrading – when the software is no longer supported.

Meanwhile, although Epicor’s leadership would say they’re ahead of the curve tech-wise, or at least on par with their weightier rivals (the likes of Oracle and SAP), they’d also concede this conflict has held them back. Painful upgrades and software flaws, among other issues, have clouded the company’s past.

But the overall tone at Insights 2019 was one of optimism, as Epicor seeks to finally hit the elusive billion-dollar-company mark within the next 12 months. The defensive mentality of old has been discarded, with a more proactive one picked up in its place.

But reckoning with the pace of growth has posed another headache. Epicor is a much larger firm than it was several years ago, but many of the internal processes are still unchanged from that of a small business. Internal change remains a learning process, and executives are finding they can’t whip up their conference speech a day before their keynote anymore.

These next 12 months will be especially interesting for a software firm that has put its chips on the table for public cloud. Epicor’s main predicament in future will centre on wrestling with legacy attitudes among a significant minority of customers while trying to keep its tech as fresh as the biggest players in ERP.