Personal blog of Yzmir Ramirez

At its Think Digital conference, IBM and Red Hat today announced a number of new services that all center around 5G edge and AI. The fact that the company is focusing on these two areas doesn’t come as a surprise, given that both edge and AI are two of the fastest-growing businesses in enterprise computing. Virtually every telecom company is now looking at how to best capitalize on the upcoming 5G rollouts, and most forward-looking enterprises are trying to figure out how to best plan around this for their own needs.

As IBM’s recently minted president Jim Whitehurst told me ahead of today’s announcement, he believes that IBM (in combination with Red Hat) is able to offer enterprises a very differentiated service because, unlike the large hyper clouds, IBM isn’t interested in locking these companies into a homogeneous cloud.

“Where IBM is competitively differentiated, is around how we think about helping clients on a journey to what we call hybrid cloud,” said Whitehurst, who hasn’t done a lot of media interviews since he took the new role, which still includes managing Red Hat. “Honestly, everybody has hybrid clouds. I wish we had a more differentiated term. One of the things that’s different is how we’re talking about how you think about an application portfolio that, by necessity, you’re going to have in multiple ways. If you’re a large enterprise, you probably have a mainframe running a set of transactional workloads that probably are going to stay there for a long time because there’s not a great alternative. And there’s going to be a set of applications you’re going to want to run in a distributed environment that need to access that data — all the way out to you running a factory floor and you want to make sure that the paint sprayer doesn’t have any defects while it’s painting a door.”

BARCELONA, CATALONIA, SPAIN – 2019/02/25: The IBM logo is seen during MWC 2019. (Photo by Paco Freire/SOPA Images/LightRocket via Getty Images)

He argues that IBM, at its core, is all about helping enterprises think about how to best run their workloads software, hardware and services perspective. “Public clouds are phenomenal, but they are exposing a set of services in a homogeneous way to enterprises,” he noted, while he argues that IBM is trying to weave all of these different pieces together.

Later in our discussion, he argued that the large public clouds essentially force enterprises to fit their workloads to those clouds’ service. “The public clouds do extraordinary things and they’re great partners of ours, but their primary business is creating these homogeneous services, at massive volumes, and saying ‘if your workloads fit into this, we can run it better, faster, cheaper etc.’ And they have obviously expanded out. They’ve added services. They are not saying we can put a box on-premise, but you’re still fitting into their model.”

On the news side, IBM is launching new services to automate business planning, budgeting and forecasting, for example, as well as new AI-driven tools for building and running automation apps that can handle routine tasks either autonomously or with the help of a human counterpart. The company is also launching new tools for call-center automation.

The most important AI announcement is surely Watson AIOps, though, which is meant to help enterprises detect, diagnose and respond to IT anomalies in order to reduce the effects of incidents and outages for a company.

On the telco side, IBM is launching new tools like the Edge Application Manager, for example, to make it easier to enable AI, analytics and IoT workloads on the edge, powered by IBM’s open-source Open Horizon edge computing project. The company is also launching a new Telco Network Cloud manager built on top of Red Hat OpenShift and the ability to also leverage the Red Hat OpenStack Platform (which remains to be an important platform for telcos and represents a growing business for IBM/Red Hat). In addition, IBM is launching a new dedicated IBM Services team for edge computing and telco cloud to help these customers build out their 5G and edge-enabled solutions.

Telcos are also betting big on a lot of different open-source technologies that often form the core of their 5G and edge deployments. Red Hat was already a major player in this space, but the acquisition has only accelerated this, Whitehurst argued. “Since the acquisition […] telcos have a lot more confidence in IBM’s capabilities to serve them long term and be able to serve them in mission-critical context. But importantly, IBM also has the capability to actually make it real now.”

A lot of the new telco edge and hybrid cloud deployments, he also noted, are built on Red Hat technologies but built by IBM, and neither IBM nor Red Hat could have really brought these to fruition in the same way. Red Hat never had the size, breadth and skills to pull off some of these projects, Whitehurst argued.

Whitehurst also argued that part of the Red Hat DNA that he’s bringing to the table now is helping IBM to think more in terms of ecosystems. “The DNA that I think matters a lot that Red Hat brings to the table with IBM — and I think IBM is adopting and we’re running with it — is the importance of ecosystems,” he said. “All of Red Hat’s software is open source. And so really, what you’re bringing to the table is ecosystems.”

It’s maybe no surprise then that the telco initiatives are backed by partners like Cisco, Dell Technologies, Juniper, Intel, Nvidia, Samsung, Packet, Equinix, Hazelcast, Sysdig, Turbonomics, Portworx, Humio, Indra Minsait, EuroTech, Arrow, ADLINK, Acromove, Geniatech, SmartCone, CloudHedge, Altiostar, Metaswitch, F5 Networks and ADVA.

In many ways, Red Hat pioneered the open-source business model and Whitehurst argued that having Red Hat as part of the IBM family means it’s now easier for the company to make the decision to invest even more in open source. “As we accelerate into this hybrid cloud world, we’re going to do our best to leverage open-source technologies to make them real,” he added.

Artificial intelligence is quickly becoming a foundational technology for enterprise software development and startups have begun addressing a variety of issues around using AI to make software and processes much more efficient.

To that end, we are delighted to announce that Jocelyn Goldfein, a Managing Director at Zetta Venture Partners will be joining on us a panel to discuss AI in the enterprise. It will take place at the TechCrunch Sessions: Enterprise show on September 5 at the Yerba Buena Center in San Francisco.

It’s not just startups that are involved in AI in the enterprise. Some of the biggest names in enterprise software including Salesforce Einstein, Adobe Sensei and IBM Watson have been addressing the need for AI to help solve the enterprise data glut.

Computers can process large amounts of information much more quickly than humans, and as enterprise companies generate increasing amounts of data, they need help understanding it all as the volume of information exceeds human capacity to sort through it.

Goldfein brings a deep engineering background to her investment work. She served as a VP of engineering at VMware and as an engineering director at Facebook, where she led the project that adopted machine learning for the News Feed ranker, launched major updates in photos and search, and helped spearhead Facebook’s pivot to mobile. Goldfein drove significant reforms in Facebook hiring practices and is a prominent evangelist for women in computer science. As an investor, she primarily is focused on startups using AI to take more efficient approaches to infrastructure, security, supply chains and worker productivity.

At TC Sessions: Enterprise, she’ll be joining Bindu Reddy from Reality Engines along with other panelists to discuss the growing role of AI in enterprise software with TechCrunch editors. You’ll learn why AI startups are attracting investor attention and how AI in general could fundamentally transform enterprise software.

Prior to joining Zetta, Goldfein had stints at Facebook and VMware, as well as startups Datify, MessageOne and Trilogy/pcOrder.

Early Bird tickets to see Joyce at TC Sessions: Enterprise are on sale for just $249 when you book here; but hurry, prices go up by $100 soon! Students, grab your discounted tickets for just $75 here.

With its latest $34 billion acquisition of Red Hat, IBM may have found something more elementary than “Watson” to save its flagging business.

Though the acquisition of Red Hat  is by no means a guaranteed victory for the Armonk, N.Y.-based computing company that has had more downs than ups over the five years, it seems to be a better bet for “Big Blue” than an artificial intelligence program that was always more hype than reality.

Indeed, commentators are already noting that this may be a case where IBM finally hangs up the Watson hat and returns to the enterprise software and services business that has always been its core competency (albeit one that has been weighted far more heavily on consulting services — to the detriment of the company’s business).

Watson, the business division focused on artificial intelligence whose public claims were always more marketing than actually market-driven, has not performed as well as IBM had hoped and investors were losing their patience.

Critics — including analysts at the investment bank Jefferies (as early as one year ago) — were skeptical of Watson’s ability to deliver IBM from its business woes.

As we wrote at the time:

Jefferies pulls from an audit of a partnership between IBM Watson and MD Anderson as a case study for IBM’s broader problems scaling Watson. MD Anderson cut its ties with IBM after wasting $60 million on a Watson project that was ultimately deemed, “not ready for human investigational or clinical use.”

The MD Anderson nightmare doesn’t stand on its own. I regularly hear from startup founders in the AI space that their own financial services and biotech clients have had similar experiences working with IBM.

The narrative isn’t the product of any single malfunction, but rather the result of overhyped marketing, deficiencies in operating with deep learning and GPUs and intensive data preparation demands.

That’s not the only trouble IBM has had with Watson’s healthcare results. Earlier this year, the online medical journal Stat reported that Watson was giving clinicians recommendations for cancer treatments that were “unsafe and incorrect” — based on the training data it had received from the company’s own engineers and doctors at Sloan-Kettering who were working with the technology.

All of these woes were reflected in the company’s latest earnings call where it reported falling revenues primarily from the Cognitive Solutions business, which includes Watson’s artificial intelligence and supercomputing services. Though IBM chief financial officer pointed to “mid-to-high” single digit growth from Watson’s health business in the quarter, transaction processing software business fell by 8% and the company’s suite of hosted software services is basically an afterthought for business gravitating to Microsoft, Alphabet, and Amazon for cloud services.

To be sure, Watson is only one of the segments that IBM had been hoping to tap for its future growth; and while it was a huge investment area for the company, the company always had its eyes partly fixed on the cloud computing environment as it looked for areas of growth.

It’s this area of cloud computing where IBM hopes that Red Hat can help it gain ground.

“The acquisition of Red Hat is a game-changer. It changes everything about the cloud market,” said Ginni Rometty, IBM Chairman, President and Chief Executive Officer, in a statement announcing the acquisition. “IBM will become the world’s number-one hybrid cloud provider, offering companies the only open cloud solution that will unlock the full value of the cloud for their businesses.”

The acquisition also puts an incredible amount of marketing power behind Red Hat’s various open source services business — giving all of those IBM project managers and consultants new projects to pitch and maybe juicing open source software adoption a bit more aggressively in the enterprise.

As Red Hat chief executive Jim Whitehurst told TheStreet in September, “The big secular driver of Linux is that big data workloads run on Linux. AI workloads run on Linux. DevOps and those platforms, almost exclusively Linux,” he said. “So much of the net new workloads that are being built have an affinity for Linux.”

IBM has launched a software service that scans AI systems as they work in order to detect bias and provide explanations for the automated decisions being made — a degree of transparency that may be necessary for compliance purposes not just a company’s own due diligence.

The new trust and transparency system runs on the IBM cloud and works with models built from what IBM bills as a wide variety of popular machine learning frameworks and AI-build environments — including its own Watson tech, as well as Tensorflow, SparkML, AWS SageMaker, and AzureML.

It says the service can be customized to specific organizational needs via programming to take account of the “unique decision factors of any business workflow”.

The fully automated SaaS explains decision-making and detects bias in AI models at runtime — so as decisions are being made — which means it’s capturing “potentially unfair outcomes as they occur”, as IBM puts it.

It will also automatically recommend data to add to the model to help mitigate any bias that has been detected.

Explanations of AI decisions include showing which factors weighted the decision in one direction vs another; the confidence in the recommendation; and the factors behind that confidence.

IBM also says the software keeps records of the AI model’s accuracy, performance and fairness, along with the lineage of the AI systems — meaning they can be “easily traced and recalled for customer service, regulatory or compliance reasons”.

For one example on the compliance front, the EU’s GDPR privacy framework references automated decision making, and includes a right for people to be given detailed explanations of how algorithms work in certain scenarios — meaning businesses may need to be able to audit their AIs.

The IBM AI scanner tool provides a breakdown of automated decisions via visual dashboards — an approach it bills as reducing dependency on “specialized AI skills”.

However it is also intending its own professional services staff to work with businesses to use the new software service. So it will be both selling AI, ‘a fix’ for AI’s imperfections, and experts to help smooth any wrinkles when enterprises are trying to fix their AIs… Which suggests that while AI will indeed remove some jobs, automation will be busy creating other types of work.

Nor is IBM the first professional services firm to spot a business opportunity around AI bias. A few months ago Accenture outed a fairness tool for identifying and fixing unfair AIs.

So with a major push towards automation across multiple industries there also looks to be a pretty sizeable scramble to set up and sell services to patch any problems that arise as a result of increasing use of AI.

And, indeed, to encourage more businesses to feel confident about jumping in and automating more. (On that front IBM cites research it conducted which found that while 82% of enterprises are considering AI deployments, 60% fear liability issues and 63% lack the in-house talent to confidently manage the technology.)

In additional to launching its own (paid for) AI auditing tool, IBM says its research division will be open sourcing an AI bias detection and mitigation toolkit — with the aim of encouraging “global collaboration around addressing bias in AI”.

“IBM led the industry in establishing trust and transparency principles for the development of new AI technologies. It’s time to translate principles into practice,” said David Kenny, SVP of cognitive solutions at IBM, commenting in a statement. “We are giving new transparency and control to the businesses who use AI and face the most potential risk from any flawed decision making.”

More than seven years after IBM Watson beat a couple of human Jeopardy! champions, the company has continued to make hay with the brand. Watson, at its core, is simply an artificial intelligence engine and while that’s not trivial by any means, neither is it the personified intelligence that their TV commercials would have the less technically savvy believe.

These commercials contribute to this unrealistic idea that humans can talk to machines in this natural fashion. You’ve probably seen some. They show this symbol talking to humans in a robotic voice explaining its capabilities. Some of the humans include Bob Dylan, Serena Williams and Stephen King.

In spite of devices like Alexa and Google Home, we certainly don’t have machines giving us detailed explanations, at least not yet.

IBM would probably be better served aiming its commercials at the enterprises it sells to, rather than the general public, who may be impressed by a talking box having a conversation with a star. However, those of us who have at least some understanding of the capabilities of such tech, and those who buy it, don’t need such bells and whistles. We need much more practical applications. While chatting with Serena Williams about competitiveness may be entertaining, it isn’t really driving home the actual value proposition of this tech for business.

The trouble with using Watson as a catch-all phrase is that it reduces the authenticity of the core technology behind it. It’s not as though IBM is alone in trying to personify its AI though. We’ve seen the same thing from Salesforce with Einstein, Microsoft with Cortana and Adobe with Sensei. It seems that these large companies can’t deliver artificial intelligence without hiding it behind a brand.

The thing is this though, this is not a consumer device like the Amazon Echo or Google Home. It’s a set of technologies like deep learning, computer vision and natural language processing, but that’s hard to sell, so these companies try to put a brand on it like it’s a single entity.

Just this week, at the IBM Think Conference in Las Vegas, we saw a slew of announcements from IBM that took on the Watson brand. That included Watson Studio, Watson Knowledge Catalog, Watson Data Kits and Watson Assistant. While they were at it, they also announced they were beefing up their partnership Apple with — you guessed it — Watson and Apple Core ML. (Do you have anything without quite so much Watson in it?)

Marketers gonna market and there is little we can do, but when you overplay your brand, you may be doing your company more harm than good. IBM has saturated the Watson brand, and might not be reaching the intended audience as a result.

Apple and IBM may seem like an odd couple, but the two companies have been working closely together for several years now. That has involved IBM sharing its enterprise expertise with Apple and Apple sharing its design sense with IBM. The companies have actually built hundreds of enterprise apps running on iOS devices. Today, they took that friendship a step further when they announced they were providing a way to combine IBM Watson machine learning with Apple Core ML to make the business apps running on Apple devices all the more intelligent.

The way it works is that a customer builds a machine learning model using Watson, taking advantage of data in an enterprise repository to train the model. For instance, a company may want to help field service techs point their iPhone camera at a machine and identify the make and model to order the correct parts. You could potentially train a model to recognize all the different machines using Watson’s image recognition capability.

The next step is to convert that model into Core ML and include it in your custom app. Apple introduced Core ML at the Worldwide Developers Conference last June as a way to make it easy for developers to move machine learning models from popular model building tools like TensorFlow, Caffe or IBM Watson to apps running on iOS devices.

After creating the model, you run it through the Core ML converter tools and insert it in your Apple app. The agreement with IBM makes it easier to do this using IBM Watson as the model building part of the equation. This allows the two partners to make the apps created under the partnership even smarter with machine learning.

“Apple developers need a way to quickly and easily build these apps and leverage the cloud where it’s delivered. [The partnership] lets developers take advantage of the Core ML integration,” Mahmoud Naghshineh, general manager for IBM Partnerships and Alliances explained.

To make it even easier, IBM also announced a cloud console to simplify the connection between the Watson model building process and inserting that model in the application running on the Apple device.

Over time, the app can share data back with Watson and improve the machine learning algorithm running on the edge device in a classic device-cloud partnership. “That’s the beauty of this combination. As you run the application, it’s real time and you don’t need to be connected to Watson, but as you classify different parts [on the device], that data gets collected and when you’re connected to Watson on a lower [bandwidth] interaction basis, you can feed it back to train your machine learning model and make it even better,” Naghshineh said.

The point of the partnership has always been to use data and analytics to build new business processes, by taking existing approaches and reengineering them for a touch screen.

“This adds a level of machine learning to that original goal moving it forward to take advantage of the latest tech. “We are taking this to the next level through machine learning. We are very much on that path and bringing improved accelerated capabilities and providing better insight to [give users] a much greater experience,” Naghshineh said.

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