CES 2019 Ramifications for Enterprise IT

Vendors and Organizations Mentioned: IBM, Ose, WindRiver, Velodyne, UV Partners, TDK Corporation, Chirp Microsystems, Qualcomm, Intel, Zigbee Alliance, Thread Group, Impossible Foods

The CES (Consumer Electronics Show) is traditionally known as the center of consumer technology. Run by the CTA (Consumer Technology Association) in Las Vegas, this show brings out enormous volumes of new technology ranging from smart cars to smart homes to smart sports equipment to smart… well, you get the picture. But within all of these announcements, there were also a number of important announcements that affect the enterprise IT world and the definition of IT that will be important for tech professionals to think about in 2019. Amalgam Insights went through hundreds of different technology press releases and announcements to find the most important announcements that will affect your professional career.

Come along with me as we look at Quantum Computing, Gender Equality, Autonomous Vehicles, Disinfected Smartphones, Low Power Virtual Reality, Neural Net Chips, Internet of Things Interoperability, and, yes, the Impossible Burger.

Quantum Computing

On January 8th, 2019, IBM announced IBM Q System One, the “first integrated universal approximate quantum computing system” designed for commercial use. From a practical perspective, this will allow R&D departments to actually have their own quantum computers. Today, the vast majority of quantum computing work is done based on remote access either to quantum computers or quantum computing emulators, which provide limits on the experimenters’ abilities to customize and configure their computing environments.

To create a quantum computing system, IBM had to bring together hardware that provided high-quality and low-error rate qubits, cryogenic equipment to cool the hardware and quantum activity, as well as the electronics, firmware, and traditional computing capabilities needed to support a quantum environment. Of course, IBM is not new to quantum computing and has been a market leader in this emerging category.

Quantum computing fundamentally matters because we are running up against the physical limits of material science that allow microprocessors to get smaller and faster, which we typically sum up as Moore’s Law. In addition, quantum computing potentially allows both for more secure encryption or the ability to quickly decrypt extremely secure technologies, depending on whether one takes a white-hat or black-hat approach. But the ramifications mean that it is important for security organizations to both start understanding quantum computing and to either stay ahead of black-hat quantum computing efforts or provide white-hat security answers to stay ahead.

Gender Equality at CES

At CES, a woman-designed sex toy originally given an innovation award (Warning: may not be Safe For Work) had its award revoked. The Ose vibrator designed by Lora DiCarlo was entered in the robotics and drone category based on its design by a robotics lab at Oregon State University and eight patents pending for a variety of robotic and biomimicry capabilities.

The product was undoubtedly risque. But CES has previously allowed virtual reality pornography to be shown within the show as well as other anatomical simulations designed for sex.

Given CES’ historical standards for other exhibitors to present similar products and objects, the revoking of this award looks biased. This is an important lesson that the answer to providing a gender-equal environment is not necessarily to simply remove all sexual content. The goal is to eliminate harassment and abuse while providing equal opportunity across gender. As long as sex is a part of consumer technology, CES needs to provide equal opportunity for all genders to present.

Autonomous Vehicles

There were a number of announcements associated with Lidar sensors and edge computing innovations. Two that got Amalgam Insights’ attention included:

WindRiver’s integration of its Chassis automotive software with its TitaniumCloud virtualization software. This announcement hints at the need for the car, as computing system, to be integrated with the cloud. This integration will be important as car manufacturers seek to upgrade car capabilities. As we continue to think about the car both as an autonomous data center of its own and set of computing and processing workloads that need to be upgraded on a regular basis, we will need to consider how the operational technologies associated with autonomous vehicles and other “Things” integrate with carrier-grade and public clouds.

Velodyne announced an end-to-end Lidar solution that includes both a hemisphere Lidar sensor called VelaDome as well as its Velia software. This launch reflects the need for hardware components and software to be integrated in the vehicle world, just as it is in the appliances and virtual machines we often use in the world of IT. This is another data point showing how autonomous vehicles are coming closer to our world of IT both in creating integrated solutions and in requiring IT-like support in the future.

Disinfected Smartphones

UV Partners announced a new product called the UV Angel Aura Clean & Charge, which combines both wireless charging with ultraviolet light disinfection. This product matters because, quite frankly, mobile phones tend to be filthy. That’s what happens when people are holding them for hours a day and rarely wash or disinfect the phones. So, this device will be useful for germophobes.

But there is also the practical aspect of being able to clean phone surfaces with this object more easily. This may lead to being able to use the phone to detect biological matter or changes more effectively without additional dirt and biocontaminants. This could make phones or other sensors more accurate in trying to detect trace elements or compounds and increase the functionality of both phones and “Things” as a result.

Low Power Virtual Reality

TDK Corporation announced its work with Qualcomm through the group company of Chirp Microsystems to improve controller tracking for mobile virtual reality and augmented reality headsets (). Most importantly, the tracking system used for these devices is only several miiliwatts, which is a small fraction of the total power within a standard smartphone battery. This compares to several hundred milliwatts for a standard optical tracking system. With this primary technology in development, both AR and VR experiences become more usable simply because they will take significantly less power to support.

This change may not sound exciting, but Amalgam Insights believes that one of the key challenges to the adoption of AR and VR is simply the battery life needed to use these applications for any extended amount of time. This breakthrough could significantly extend the life of AR and VR apps.

Artificial Intelligence

Intel made a number of chip announcements. Amalgam Insights is not a hardware analyst firm, so most of the mobile and laptop-based announcements are beyond our coverage. But the announcement that got our attention was the Intel Nervana Neural Network Processor. This chip, developed with Facebook, is developed to accelerate the detection of inference associated with the algorithmic processing of neural nets and will drive higher performance machine learning and artificial intelligence efforts.

At a time when every chip player is trying to get ahead with GPUs and TPUs, Intel is making its mark by focusing on the detection of iterative inference, which is a necessary part of the “intelligence” of AI. Amalgam Insights looks forward to seeing how the Nervana processor is made available for commercial use and as a cloud-based capability for the enterprise world.

Internet of Things Interoperability

The Zigbee Alliance and Thread Group announced completing the Dotdot 1.0 specification, which will improve interoperability across smart home devices and networks made by different vendors. By providing a standard application layer that works across a wide variety of vendors and works on an IP networking standard, Dotdot brings a level of standardization to application-level configuration, testing, and certification.

This standard is an important step forward for companies working on Smart Home devices or related Smart Office devices and seeking a common way to ensure that new devices will be able to communicate with existing device investments. Amalgam Insights looks forward to seeing how this standard revolutionizes Smart Buildings and the Future of Work.

And, the Impossible Burger

The belle of the ball, so to speak, at CES was the Impossible Burger 2.0, a soy-based protein held together by heme with iron and protein content similar to beef.

So, this is very cool, but why is this relevant to IT? First, this burger reminds us that food is now tech. Think about both how interesting and weird this is. A company has made custom proteins to build a new type of food designed to replace the taste and role of beef. Or at least that’s where they are today.

Meanwhile in the IT world, identity is increasingly based on biometrics: eyes, fingerprints, facial recognition. It is only a matter of time before either protein or DNA profiles are added to this mix. There will undoubtedly be some controversies and hiccups as this happens, but it is almost inevitable given the types of sensors we have and the evolution of DNA technologies like CRISPR that rapidly sequence and cut up DNA.

So, as we get better at replicating the nutrition and texture of meat with plant-based proteins at the same time that our physical bodies are increasingly used to provide access to our accounts… yes, this gets weird. But we’re probably five-to-ten years away from being hacked by some combination of these technbologies as the DNA, protein, and biometric worlds keep coming closer and closer together.

For now, this is just cool to watch. And the Impossible Burger 2.0 sounds like a great vegan alternative to a burger. But putting the pieces together, identity in 2030 is going to be extremely difficult to manage.

Amazon Expands Toolkit of Machine Learning Services at AWS re:Invent

At AWS re:Invent, Amazon Web Services expanded its toolkit of machine learning application services with the announcements of Amazon Comprehend Medical, Amazon Forecast, Amazon Personalize, and Amazon Textract. These new services augment the capabilities Amazon provides to end users when it comes to text analysis, personalized recommendations, and time series forecasts. The continued growth of these individual services removes obstacles for companies looking to get started with common machine learning tasks on a smaller scale; rather than building a wholesale data science pipeline in-house, these services allow companies to quickly get one task done, and this permits an incremental introduction to machine learning for a given organization. Forecast, Personalize, and Textract are in preview, while Comprehend Medical is available now.

Amazon Comprehend Medical, Forecast, Personalize, and Textract join a collection of machine learning services that include speech recognition (Transcribe) and translation (Translate), speech-to-text and text-to-speech (Lex and Polly) to power machine conversation such as chatbots and Alexa, general text analytics (Comprehend), and image and video analysis (Rekognition).

New Capabilities

Amazon Personalize lets developers add personalized recommendations into their apps, based on a given activity stream from that app and a corpus of what’s available to be recommended, whether that’s products, articles, or other things. In addition to recommendations, Personalize can also be used to customize search results and notifications. By combining a given search string or location with contextual behavior data, Amazon looks to provide customers with the ability to build trust.

Amazon Forecast builds private, custom time-series forecast models that predict future trends based on that data. Customers provide both histoical data and related causal data, and Forecast analyzes the data to determine the relevant factors in building its models and providing forecasts.

Amazon Textract extracts text and data from scanned documents, without requiring manual data entry or custom code. In particular, using machine learning to recognize when data is in a table or form field and treat it appropriately will save a significant amount of time over the current OCR standard.

Finally, Amazon Comprehend Medical, an extension of last year’s Amazon Comprehend, uses natural language processing to analyze unstructured medical text such as doctor’s notes or clinical trial records, and extract relevant information from this text.

Recommendations

Organizations doing resource planning, financial planning, or other similar forecasting that currently lack the capability to do time series forecasting in-house should consider using Amazon Forecast to predict product demand, staffing levels, inventory levels, material availability, and to perform financial forecasting. Outsourcing the need to build complex forecasting models in-house lets departments focus on the predictions.

Consumer-oriented organizations looking to build higher levels of engagement with their customers who provide generic, uncontextualized recommendations right now (based on popularity or other simple measures) should consider using Amazon Personalize to provide personalized recommendations, search results, and notifications via their apps and website. Providing high-quality relevant recommendations a la minute builds customer trust in the quality of a given organization’s engagement efforts, particularly compared to the average spray-and-pray marketing communication.

Organizations that still depend on physical documents, or who have an archive of physical documents to scan and analyze, should consider using Amazon Textract. OCR’s limits are well-known, especially when it comes to accurately interpreting and formatting semi-structured blocks of text data such as form fields and tables, resulting in significant time devoted to post-processing manual correction. Textract handles complex documents without the need for custom code or maintaining templates; being able to automate text interpretation and analysis further accelerates document processing workflows, and better permits organizations to maintain compliance.

Medical organizations using software that depends on manually-implemented rules to process their medical text should consider using Amazon Comprehend Medical. By removing the need to maintain a list of rules in-house, Comprehend Medical accelerates the ability to extract and analyze medical information from unstructured text fields like doctor’s notes and health records, improving processes such as medical coding, cohort analysis to recruit patients for clinical trials, and health monitoring of patients.

All organizations looking to use machine learning services from external providers need to consider whether outsourcing will work for their circumstances. Data privacy is a key concern, and even more so in regulated verticals with industry-specific rules such as HIPAA. Does the service you want to use respect those rules? From a compliance perspective, why a model gives the results it does needs to be explained as well; merely accepting results from the black box at face value is insufficient. Machine learning products that automatically provide such an explanation in plain English do exist, but this feature is still uncommon and in its infancy.

Conclusion

With its latest announcements, Amazon continues to broaden the scope of customer issues it addresses with machine learning services. Medical companies need better text analytics yesterday, but struggle to comply with HIPAA while assessing the data they have. Customer-facing organizations face stiff competition when their competitor is only a click away. And any company trying to plan for the future based on past data grapples with understanding what factors affect future results. Amazon’s machine learning application services address common tactical business issues by simplifying the process for customers of implementing task-specific machine learning models to pure inputs and outputs. These services present outsourcing opportunities for overworked departments struggling to keep up.

Data Science and Machine Learning News, November 2018

On a monthly basis, I will be rounding up key news associated with the Data Science Platforms space for Amalgam Insights. Companies covered will include: Alteryx, Amazon, Anaconda, Cambridge Semantics, Cloudera, Databricks, Dataiku, DataRobot, Datawatch, DominoElastic, H2O.ai, IBM, Immuta, Informatica, KNIME, MathWorks, Microsoft, Oracle, Paxata, RapidMiner, SAP, SAS, SnapLogic, Tableau, Talend, Teradata, TIBCO, Trifacta, TROVE.

Continue reading “Data Science and Machine Learning News, November 2018”

Artificial Intelligence/Machine Learning (AI/ML) and Learning Systems in the Brain

Simulating Learning Processes in the Brain With AI/ML

Key Stakeholders: Chief Learning Officers, Chief Human Resource Officers, Learning and Development Directors and Managers, Corporate Trainers, Content and Learning Product Managers.

Why It Matters: The skills necessary for success in the corporate world are varied and include hard skills, people skills and situational awareness. While L&D is embracing the use of AI/ML to analyze learners’ data and to personalize learning paths, curate effective content, and attempt to better engage learners, what L&D has failed to embrace is the application of AI/ML to model each of these distinct learning systems, and their interactions.

Top Takeaway: Corporate learning vendors would be well served to develop AI/ML models that capture the processing characteristics of the three learning systems in the brain known to mediate hard skills, soft skills, and situational awareness learning. A comprehensive AI/ML model that captured the processing characteristics of each of these three distinct learning systems could be used to develop and test products and tools that optimize content curation, learning paths, engagement, and delivery processes that will differ substantially across systems and tasks to be learned.

Vendors with the Skillset and Expertise to Build this AI/ML Tool: Cornerstone, CrossKnowledge, IBM, Infor, LTG, Oracle, Saba, Salesforce, SAP, Workday, and likely many others.

Artificial Intelligence/Machine Learning and L&D

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Data Science and Machine Learning News, October 2018

On a monthly basis, I will be rounding up key news associated with the Data Science Platforms space for Amalgam Insights. Companies covered will include: Alteryx, Anaconda, Cambridge Semantics, Cloudera, Databricks, Dataiku, DataRobot, Datawatch, DominoElastic, H2O.ai, IBM, Immuta, Informatica, KNIME, MathWorks, Microsoft, Oracle, Paxata, RapidMiner, SAP, SAS, Tableau, Talend, Teradata, TIBCO, Trifacta, TROVE.

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Todd Maddox Ph.D.’s Top Four Scientific Observations on DevLearn 2018

If you have a passion for learning then DevLearn is for you. DevLearn 2018 was quite the event. With excellent keynote addresses, breakout sessions, numerous vendors and great demos it was action-packed. I enjoyed every minute of DevLearn 2018 and I am already looking forward to 2019.

I took a few days to gather my notes and thoughts, and I have a number of observations on DevLearn 2018. I am sure that others who attended DevLearn 2018 will highlight different topics, and acknowledging that I was only able to speak in detail with a dozen or so vendors, here are my Top Four Scientific Observations.

Whether Talent, Behavioral or Data……The Impact of Science Continues to Grow

Relevant Vendors That I Spoke With: Adobe, Allego, EdCast, Inkling, iSpring, Learning Tribes, LEO Learning, MPS Interactive, Mursion, OttoLearn, Rehearsal, Schoox, STRIVR, Valamis

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Why It Matters that IBM Announced Trust and Transparency Capabilities for AI


Note: This blog is a followup to Amalgam Insights’ visit to the “Change the Game” event held by IBM in New York City.

On September 19th, IBM announced its launch of a portfolio of AI trust and transparency capabilities. This announcement got Amalgam Insight’s attention because of IBM’s relevance and focus in the enterprise AI market throughout this decade.  To understand why IBM’s specific launch matters, take a step back in considering IBM’s considerable role in building out the current state of the enterprise AI market.

IBM AI in Context

Since IBM’s public launch of IBM Watson on Jeopardy! in 2011, IBM has been a market leader in enterprise artificial intelligence and spent billions of dollars in establishing both IBM Watson and AI. This has been a challenging path to travel as IBM has had to balance this market-leading innovation with the financial demands of supporting a company that brought in $107 billion in revenue in 2011 and has since seen this number shrink by almost 30%.

In addition, IBM had to balance its role as an enterprise technology company focused on the world’s largest workloads and IT challenges with launching an emerging product better suited for highly innovative startups and experimental enterprises. And IBM also faced the “cloudification” of enterprise IT in general, where the traditional top-down purchase of multi-million dollar IT portfolios is being replaced by piecemeal and business-driven purchases and consumption of best-in-breed technologies.

Seven years later, the jury is still out on how AI will ultimately end up transforming enterprises. What we do know is that a variety of branches of AI are emerging, including

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Learning Elastic’s Machine Learning Story at Elastic{ON} in Boston

Why is a Data Science and Machine Learning Analyst at Elastic’s road show when they’re best known for search? In early September, Amalgam Insights attended Elastic{ON} in Boston, MA. Prior to the show, my understanding of Elastic was that they were primarily a search engine company. Still, the inclusion of a deep dive into machine…

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Oracle GraphPipe: Expediting and Standardizing Model Deployment and Querying

On August 15, 2018, Oracle announced the availability of GraphPipe, a network protocol designed to transmit machine learning data between remote processes in a standardized manner, with the goal of simplifying the machine learning model deployment process. The spec is now available on Oracle’s GitHub, along with clients and servers that have implemented the spec for Python and Go (with a Java client soon to come); and a TensorFlow plugin that allows remote models to be included inside TensorFlow graphs.

Oracle’s goal with GraphPipe is to standardize the process of model deployment regardless of the frameworks utilized in the model creation stage.

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Data Science Platforms News Roundup, July 2018

On a monthly basis, I will be rounding up key news associated with the Data Science Platforms space for Amalgam Insights. Companies covered will include: Alteryx, Anaconda, Cloudera, Databricks, Dataiku, DataRobotDatawatch, Domino, H2O.ai, IBM, Immuta, Informatica, KNIME, MathWorks, Microsoft, Oracle, Paxata, RapidMiner, SAP, SAS, Tableau, Talend, Teradata, TIBCO, Trifacta.

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