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Friday, May 25, 2018

Chatbots for Setting up and Maintaining Meetings

For some time have been using assistants on Siri, Alexa and Google to manage meetings.   And several times its been suggested that this kind of very common activity should be done by a meeting chatbot.  I agreed, but quickly found it was far less than trivial.  Sounds simple, but the complexity assistant logic among multiple human and machine agents is hard.    Which further suggests that further extending other multi agent intelligent conversations are not easy either.

Read this article for a good intro:

However trivial it may sound, creating an AI program to successfully schedule meetings is a monstrously difficult challenge. But the employees of X.ai are some of the most dedicated nerds you’ll ever meet. 

Author:  John H. Richardson in Wired. 
AI Chatbots try to schedule Meetings without Enraging us.

McKinsey AI Frontier: 400 Use Cases

Mostly non-technical:

McKinsey: Notes from the AI frontier: Applications and value of deep learning

An analysis of more than 400 use cases across 19 industries and nine business functions highlights the broad use and significant economic potential of advanced AI techniques. ... " 

Thursday, May 24, 2018

Blockchains Talking to Each Other

For backups, and for other housekeeping and updating functions?  For assembling contracts among component pieces?  Smart Contracts are mentioned as a form of Blockchain architecture being enabled.

How to get blockchains to talk to each other
If blockchains are really going to give us the internet of money, they’ll need to work together.
by Mike Orcutt in Technology Review ... 

".... Hardjono and two colleagues at MIT argue in a new paper (PDF) that today’s blockchain developers should borrow a concept from the internet protocol suite called the datagram, which is a common unit of information that can move across different networks. “Every network that sees it knows how to parse it and knows how to forward it,” Hardjono says. “What is the datagram equivalent for blockchain systems?” .... " 

When to Hold Em, When to Compete

Nash Equilibrium's use in Competitive Situations is re-examined, with hope for its use in competitive behavior situations.  We looked at it for that and found no golden egg, but it doesn't mean others couldn't find it.   A reexamination.   Complexity technical.  Just because optimum solutions are known to be probably impossibly hard, very good solutions are probably better than what we are doing today.

When to Hold 'Em    By CACM Staff 
Communications of the ACM, Vol. 61 No. 6, Pages 6-7

Neil Savage deserves praise for his informative overview of recent computational results related to Nash equilibrium in his news story "Always Out of Balance" (Apr. 2018). I fully agree that the notion of Nash equilibrium does not always reflect how competitors behave in competitive situations, and that the fact that Nash equilibrium is provably computationally intractable makes it less useful than John Nash himself might have envisioned when he developed it. However, Savage also overstated (somewhat) the effect of intractability by claiming the intractability of computing Nash equilibrium necessitates researchers abandon this notion in favor of other competition-related ideas.

While looking for Nash equilibrium yields additional computational complexity, the decision-making problem is, in general, already computationally intractable (NP-hard) for non-competitive situations (such as when a company makes internal planning decisions). In doing so, a company would be looking for an optimal solution (such as one that would aim to help produce maximum profit), but computational optimization is, in general, NP-hard. Such computational intractability does not mean researchers have to abandon the idea of optimization and look for other ideas. Many real-life problems are NP-hard (such as robotic movement) and what makes working on them such an intellectual and computational challenge.

Indeed, there is no general feasible algorithm (unless P = NP), so computer scientists need to be creative when designing algorithms for specific practical problems.  .... " 

Vladik Kreinovich, EL Paso, TX, USA

Dysfunctional Relationship with AI

Nice thoughts on  our relationship with Analytics and ultimately AI.  But its not new, its been changing since the industrial revolution.  The details are just getting more and more buried into the complexity.

 It’s Me, Not You – Our Dysfunctional Artificial Intelligence Relationship   By Michele Goetz, Principal Analyst  Forrester

We have a tendency to blame technology when things go wrong. I’m the first to admit that after years of working in the technology industry I’ve become more and more annoyed with the technology I use. As artificial intelligence (AI) capabilities have emerged in my smart phone keeping me on schedule, telling me how to get somewhere, or generally keeping me in line, I’ve gotten conditioned to technology just working.  Except when it doesn’t. That’s when I want to throw that phone, espresso machine, laptop or home security pad into a blender. (Yes, it was a rough morning.)

AI pioneers have provided us with a glimpse of and conditioned us to ambient AI making it hard to break up with each other. They have also set a very high bar on our expectations of what AI should do for our businesses. But, let’s understand, Google was able to do this after two decades of research, curating collections and observing our every move. Apple too has tracked our app usage, music preferences, and daily lives through its iCloud. And Facebook sees our public and private conversations, what we share, and our personal opinions. Creepy, yes, but that is another conversation.

The point is that enterprises embarking on AI need to radically shift their approach to technology adoption and analytics. This is not a plug and play and bolt on strategy. It takes work to go from POC to a capability that comes close to our expectations of AI based on our consumer experience.  .... "

Contract Guardian Acquired

A company to look at to understand the value basis of simple agreements, and where this can be taken beyond for much more value.   Note the inclusion of risk assessments, a good start beyond simple contract expirations and renewals.  Check them out.

UCG Technologies Launches into Healthcare with Acquisition of Contract Management SaaS Provider Contract Guardian, Inc.

INDEPENDENCE, Ohio, May 24, 2018 /PRNewswire/ -- UCG Technologies (UCG), a global information technology services firm, has acquired Cincinnati-based Contract Guardian, Inc. a leader in automated contract management.

Contract Guardian provides a software as a service solution that enables healthcare and cross-industry organizations to manage large numbers of contracts. The system enables clients to easily store/retrieve contracts and verify that they meet regulatory guidelines, manage signatures, workflows, automatically track expirations, review contracts, and conduct risk assessments.

The regulatory requirements healthcare technology solutions must satisfy are some of the most demanding when compared with other industries. UCG brings a level of expertise in endpoint security, backup/disaster recovery, and cloud infrastructure that will serve as a point of differentiation from other contract management solution providers. Jim Kandrac, President of UCG, comments, "The foundation of our business is built on data protection. We recognize the importance of this in healthcare and are looking forward to leveraging our experience for healthcare and cross-industry clients."

The acquisition also provides significant expansion opportunity for UCG into the healthcare market, as healthcare comprises the majority of Contract Guardian's client base.  UCG Regional Director Matt Paterini, PharmD brings his healthcare experience to the company and comments, "After working in health system administration, I have seen many contract management challenges firsthand. It is a unique advantage to be able to provide an offering that will solve regulatory challenges and provide cost efficiencies for healthcare organizations."  ... "

Automation Requires Lifelong Learning

Agreed, most important will the kind of learning and training that will be required, which will also be a dynamically changing target.    Our own look at tasks that comprised jobs, and the continual redefinition of jobs, made that clear.

Automation Will Make Lifelong Learning a Necessary Part of Work
By Jacques Bughin, Susan Lund, Eric Hazan  in HBR ... ' 

Business Intelligence vs Operations Intelligence

Had heard the Operational Intelligence term, but was not often mentioned in the enterprise.   This piece defines both methods and how they interact.   OI could be a place to start when you are trying to define and model specific business process.    But more detail of the kind in BPM can be very useful, and get you to more detail you can improve.

In iiAnalytics Blog:

BI versus OI, A Distinction with very big difference    By Geoffrey Moore

As a reader of this blog, you are likely quite familiar with BI (Business Intelligence). It has been a foundational element of enterprise computing for over thirty years, the mainstay of iconic companies like SAS, Cognos (now IBM), and BusinessObjects (now SAP). And I expect you may also have heard of OI (Operational Intelligence), but I am willing to bet you do not have a clear sense of what precisely that latter term refers to.

Looking up Operational Intelligence in Wikipedia does not help much. The definition there blurs the distinction between BI and OI by combining attributes from each. I have reprinted it below with what I consider to be the BI attributes in blue bold and all the OI ones in red italics:

It is not that this definition is wrong. It is just that it suppresses the differences between OI and BI, differences that are key for enterprise executives to understand. I think we would all be better served, therefore, if we first began by defining Operational Intelligence in direct contrast to Business intelligence, along the following lines:

Consider the Bento Browser

Makes much sense if you do organize the information well.

Bento Browser Makes It Easier To Search On Mobile Devices
By Carnegie Mellon University, original article.

Carnegie Mellon University (CMU) researchers have developed a new Web browser that brings order to complex searches in a way not possible with conventional tabbed browsing.

The Bento browser stores each search session as a project workspace that monitors the most interest or relevant parts of visited web pages, allowing users to move from site to site without having to keep every tab open for fear of losing information. With the Bento browser, these projects are stored for later use, can be handed off to others, or can be moved to different devices, according to CMU professor Aniket Kittur.

In user studies that compared Bento with the Safari browser, users said Bento kept their search better organized.

The researchers reported on the new browser at CHI 2018, the Conference on Human Factors in Computing Systems, last week in Montreal, Canada.   .... "

Summary of AI in Retail

Been asked to look at this, here is a start.  And the continued moves in the smarter home, and as assistants.

Artificial Intelligence (AI) in Retail Market to hit $8bn by 2024    Posted by Sagar  in DSC.

Artificial Intelligence (AI) in Retail Market size is set to exceed USD 8 billion by 2024; according to a new research report by Global Market Insights, Inc. 

The AI in retail market is driven by the increasing investments in it across the globe. The growing investment in the technology is attributed to the wide applications of the AI technology along with advanced analytics, machine learning. AI is set to unleash the next phase of the digital disruption and the market participants are preparing themselves for it. The investment in the technology is growing rapidly, dominated by the tech giants such as Google, Microsoft, IBM, AWS, and Baidu. In 2016, approximately USD 30 billion investment in the technology has been witnessed, with more than 90% on the R&D activities and remaining 10% on the merger & acquisition activities. Furthermore, private equity financing, seed investment, and venture capital investment also grew significantly amounting to a cumulative total of over USD 6 billion.  .... " 

Wednesday, May 23, 2018

Samsung Wants AI Features in all of its Devices

I was was recently shopping for new home appliances, and paid attention to what the major players had in terms of integrated 'smart' capabilities.   I noted that Samsung was mentioning their Bixby assistant language in their sales material.    I asked the sales people, but they knew nothing about it, only that it would come sometime.  Were also unclear if what I would buy could be upgraded later.  Still Samsung is making some strong statements on their future.  Further what AI will mean then is still unclear.

Samsung wants AI features in all its devices by 2020
Let's just hope Bixby is working well by then.
Jon Fingas, @jonfingas in Engadget ... "

(Update) Data Science and Machine Learning for Healthcare

Cognitive Systems Institute Talk  

24 May 2018: 10:30 AM, ET   (Access Instructions below)

Talk by: Farah Shamout, Oxford University

Title: “Data Science and Machine Learning for Healthcare   

Abstract:     This talk will highlight the opportunities of AI in healthcare and how current advancements in the field are improving patient outcomes. By focusing on the use of electronic health records (EHRs), I will provide practical data preparation tips and summarize important considerations to keep in mind when using EHRs. Next, I will compare machine learning methods to Early Warning Scores, or the traditional predictors of acutely ill patients that are currently being used in hospitals. Finally, the talk will conclude with the limitations of translating research into clinical settings. 

Bio: Farah Shamout is a PhD student in Engineering Science in the Computational Health Informatics Laboratory at the University of Oxford. Shamout gained her BSc in Computer Engineering at New York University Abu Dhabi, before coming to Oxford as a Rhodes Scholar in 2016. Her DPhil focuses on machine learning systems developed using the HAVEN project, and aims to produce a hospital-wide alerting system to assess patients continuously based on machine learning methods and large-scale data acquisition from the Oxford University Hospitals NHS Foundation Trust and Portsmouth NHS Foundation Trust. Her research touches on both Bayesian nonparametrics and deep learning methods. 
Slides and Recording will be placed here.

Join the meetings by pointing your web browser to:  https://zoom.us/j/7371462221 ; Callin: (415) 762-9988 or (646) 568-7788 Meeting id 7371462221 ; International Numbers: https://zoom.us/zoomconference.

Slides and Recording will be placed here: http://cognitive-science.info/community/weekly-update/

 Join the CSIG LinkedIn Group to get reminders about talks and discuss them. Use twitter: #CSIGNews & #OpenTechAI

Replays before Dec 2015:  Dial 877.471.6587 or 402.970.2667 and enter the call’s Replay ID when prompted for a program ID number.   The Replay ID is listed in the Recording column of each date.

Benchmark Suite for Assessing Machine Learning

Ultimately key to making this work.    Measuring the results, starting with benchmarks.

How to Evaluate Machine Learning?  U of Toronto Research Supports Latest Benchmark Initiative 
U of Toronto News   By Nina Haikara

An industrial-academic consortium that includes Google, the University of Toronto (U of T) in Canada, and Harvard and Stanford universities is developing a new benchmark suite for assessing machine learning (ML) performance. U of T's Gennady Pekhimenko says the MLPerf consortium is investigating two benchmarking areas--an "open" category in which any model can be applied to a fixed dataset, and a "closed" category in which both model and datasets are fixed, making execution time, power requirements, and design-cost evaluations helpful. Pekhimenko notes his laboratory has developed an open source benchmark suite called TBD (To Be Determined) as a training benchmark for deep neural networks. "We're interested in understanding how well available hardware and software perform, but we also look at both hardware and software efficiency," he says. "We then provide hints to the ML developers, so they can make their networks more efficient, and hence develop new algorithms and insights faster." .... ' 

Unilever and WPP Collaborate for Disruptive Tech

Unilever and WPP Launch Collaboration in Singapore

SINGAPORE – Unilever and WPP have established an in-house collaboration with Unilever Foundry and its start-up community.

The partnership, which involves a flexible new Team Unilever model along with specialist input from WPP, is designed to meet the FMCG giant’s marketing needs in a period of technological change and drive innovation. ...

Peter Dart, WPP’s global team leader for Unilever, added: “We wanted to co-create a structure that allows us to work more closely with Unilever and focus on all the various services along the consumer journey that can help Unilever and its brands grow. The key is agility and the enhancing ability to respond to disruption in the consumer products market as well as in marketing services offerings.” ... " 

Authentic Emotional Intelligence

Can they be separated?  Can deep learning be yet more precise in making a distinction?   In some early work with MIT Media lab we tried to detect emotion and engagement when people interacted with product.   Can artificial neurons now do it the same way?

Is Your Emotional Intelligence Authentic, or Self-Serving?  By Ron Carucci in the HBR

It’s possible to fake emotional intelligence. Similar to knockoffs of luxury watches or handbags, there are emotions and actions that look like the real thing but really aren’t. With the best of intentions, I’ve seen smart leaders charge into sensitive interactions armed with what they believed was a combination of deep empathy, attuned listening, and self-awareness but was, in fact, a way to serve their own emotional needs. It’s important to learn to spot these forgeries, especially if you’re the forger. ..... "

Ahold Delhaize Focusing Digital Efforts

Have been impressed wth AHold efforts over the years, now with a lab for Digital:

Ahold Delhaize focuses its digital efforts
By Rachel England, @rachel_england   in Progressive Grocer

Ahold Delhaize is launching a new department in the US called Peapod Digital Labs to drive its e-commerce efforts, ramp up digital sales and hone in on its personalization efforts. The company named JJ Fleeman president and chief e-commerce officer of the new arm.  ... "

Counterfeit Goods Detector

We worked on the detection of counterfeit goods for some time, this would have been very useful. Will take a deeper look.

IBM built a handheld counterfeit goods detector
The AI tricorder knows those aren't official Yeezys.

By Rachel England, @rachel_england in Engadget

Just a month after IBM announced it's leveraging the blockchain to guarantee the provenance of diamonds, the company has revealed new AI-based technology that aims to tackle the issue of counterfeiting -- a problem that costs $1.2 trillion globally. IBM Crypto Anchor Verifier brings together AI and optical imaging to help prove the identity and authenticity of frequently forged goods such as fine wine, diamonds and medicine, as well as analyze water quality and detect bacteria, such as E.coli. And the technology is small enough to use with a cell phone camera. ... " 

Walmart Pauses on Scan & Go

Been following for a while.   Tested many checkout options.  Seems to work better at Sam than Wal-Mart.

Walmart drops Scan & Go tech – again      by George Anderson in Retailwire

Walmart’s self-checkout Scan & Go technology has been a hit with the company’s Sam’s Club members. The same cannot be said for customers at the retailer’s namesake stores. Walmart announced it has ended a test of the mobile technology in its stores.

“We’re testing things all across the country at different stores and it’s about what works best for the customer,” Ragan Dickens, a Walmart spokesperson, told the Northwest Arkansas Democrat Gazette. “We want the whole checkout process to be that seamless process. So, if there’s points in the process that are not quite there yet on the seamless front, we take those learnings and we’re plugging them into other areas of the store.” ... "

Tuesday, May 22, 2018

Thinking About Virtual Worlds

Even a simple mirror can create a virtual world.  I remember when we experimented with data immersion using VR in virtual worlds, it was remarkable to see how hard navigation was.  To the point that you often had to go back to the expected flat world to make sense of it.   This article hints at why.

The Physics of a Mirror Creates a Virtual World in Wired.
Human eyes are sort of dumb—but you can trick them into being smart ... " 

Laser Power Insect Robotics

Having very small flying robots that can be tasked to jobs, alone or in groups,  will change many things.  We examined how tasks and services might be solved by such methods.  There continue to be updates.

Laser-Powered Robot Insect Achieves Lift Off
Everything is better with lasers, especially tiny robot insects    By Evan Ackerman

For robots of all sizes, power is a fundamental problem. Any robot that moves is constrained in one way or another by power supply, whether it’s relying on carrying around heavy batteries, combustion engines, fuel cells, or anything else. It’s particularly tricky to manage power as your robot gets smaller, since it’s much more straightforward to scale these things up rather than down—and for really tiny robots (with masses in the hundreds of milligrams range), especially those that demand a lot of power, there really isn’t a good solution. In practice, this means that on the scale of small insects robots often depend on tethers for power, which isn’t ideal for making them practical in the long term.

At the IEEE International Conference on Robotics and Automation in Brisbane, Australia, next week, roboticists from the University of Washington, in Seattle, will present RoboFly, a laser-powered insect-sized flapping wing robot that performs the first (very brief) untethered flight of a robot at such a small scale. ...  "

Simplified Machine Learning

Simplified and non-technical view:

Machine learning: A quick and simple definition
Get a basic overview of machine learning and then go deeper with recommended resources.  By James Furbush in O'Reilly

The following overview covers some of the basics of machine learning (ML): what it is, how it works, and what you need to keep in mind before taking advantage of it.

This information is curated from the expert ML material available on O’Reilly’s online learning platform.  ... " 

Microsoft Makes Chat calls in China

More word of  call-making chatbots, akin to recently announced Google Duplex.  Ultimately you will have to have such systems communicating, with people and other systems, but the implications need to be thought through.  Will we always know who is calling?

Microsoft also has an AI bot that makes phone calls to humans
Similar to Google Duplex, but only in China
By Tom Warren  ... in theVerge

 " ... Google demonstrated a jaw-dropping new capability in Google Assistant earlier this month, allowing the Assistant to make calls on your behalf. While Google Duplex generated controversy and discussion around artificial intelligence, Microsoft has been testing similar technology with millions of people in China. At an AI event in London today, Microsoft CEO Satya Nadella showed off the company’s Xiaoice (pronounced “SHAO-ICE”) social chat bot.

Microsoft has been testing Xiaoice in China, and Nadella revealed the bot has 500 million “friends” and more than 16 channels for Chinese users to interact with it through WeChat and other popular messaging services. Microsoft has turned Xiaoice, which is Chinese for “little Bing,” into a friendly bot that has convinced some of its users that the bot is a friend or a human being. “Xiaoice has her own TV show, it writes poetry, and it does many interesting things,” reveals Nadella. “It’s a bit of a celebrity.” ... " 

Geographic Optimization with Bayesian Networks

Had not seen this kind of optimization before with Bayesian Networks.  Webinar leads you through the process, largely non-technical.

 ... By Stefan Conrady
Managing Partner at Bayesia USA & Singapore: Bayesian Networks for Research, Analytics, and Reasoning .... 

Geographic Optimization with Bayesian Networks and BayesiaLab
You may not know that you can use BayesiaLab for geographic optimization. Today's webinar explained how you can find an optimal location for a distribution hub that needs to connect thousands of geographically dispersed suppliers and customers. Bayesian networks and BayesiaLab make this type of optimization remarkably quick and easy. ...  "

Pets and Machine Learning Interactions

In Pete Warden's Blog, interesting views.   Have seen some of that in my own menagerie of chatbots and responsive assistants.    But I think ultimately we will want assistant than amusement.  Machine learning is collaborative in the sense that it solves narrow problems.  So does a 'Push Button' model of tech.  So will a 'pet model' be attentive and responsive?  A neighbor has a guide dog, which has been trained to be more attentive and responsive, rather than pet.   Seems more the model we will see.

Why ML interfaces will be more like pets than machines

When I talk to people about what’s happening in deep learning, I often find it hard to get across why I’m so excited. If you look at a lot of the examples in isolation, they just seem like incremental progress over existing features, like better search for photos or smarter email auto-replies. Those are great of course, but what strikes me when I look ahead is how the new capabilities build on each other as they’re combined together. I believe that they will totally change the way we interact with technology, moving from the push-button model we’ve had since the industrial revolution to something that’s more like a collaboration with our tools. It’s not a perfect analogy, but the most useful parallel I can think of is how our relationship with pets differs from our interactions with machines.

To make what I’m saying more concrete, imagine a completely made-up device for helping around the house (I have no idea if anyone’s building something like this, so don’t take it as any kind of prediction, but I’d love one if anybody does get round to it!). It’s a small indoors drone that assists with the housework, with cleaning attachments and a grabbing arm. I’ve used some advanced rendering technology to visualize a mockup below:

On the rise of the Chatbots

HPE provides an a good, non technical view.  Conversational intelligence and its increasing acceptance.  Obstacles will be maintaining the underlying knowledge.

Conversational AI and the rise of the chatbots

It’s important to understand what conversational AI is, why it’s become so popular, the obstacles, and its likely future.

You can hardly turn on the television news, pull a magazine off a rack in a doctor’s office, or check out your social media without being confronted by a discussion about artificial intelligence. Whether the writer or talking head is decrying the imminent robot apocalypse or celebrating our deep-learning-based salvation, most of the coverage has one thing in common: an imprecise definition of AI. AI is, at its base, nothing more than software that simulates intelligence.

One specific type of AI is cropping up all around the Internet: conversational AI, mostly in the form of chatbots. The most recent and high-profile news about AI was Google’s announcement that its AI, called Google Assistant, beat the Turing test—150 times. The Turing test evaluates a machine’s ability to successfully mimic human intelligence by presenting as indistinguishable from human communication. .... " 

Synthetic Data

Companies may often have mixes of real and synthetic data,  early on we used simulations to create streams of data that were realistic for particular context. Synthetic data can also be assembled from snippets of data from other sources. Behavioral data is a good example. Good to think of a plan to make this available.

Deep learning with synthetic data will democratize the tech industry
From Evan Nisselson in TechCrunch.

" .... Synthetic data is computer-generated data that mimics real data; in other words, data that is created by a computer, not a human. Software algorithms can be designed to create realistic simulated, or “synthetic,” data.

This synthetic data then assists in teaching a computer how to react to certain situations or criteria, replacing real-world-captured training data. One of the most important aspects of real or synthetic data is to have accurate labels so computers can translate visual data to have meaning.

Since 2012, we at LDV Capital have been investing in deep technical teams that leverage computer vision, machine learning and artificial intelligence to analyze visual data across any business sector, such as healthcare, robotics, logistics, mapping, transportation, manufacturing and much more. Many startups we encounter have the “cold start” problem of not having enough quality labelled data to train their computer algorithms. A system cannot draw any inferences for users or items about which it hasn’t yet gathered sufficient information.

Startups can gather their own contextually relevant data or partner with others to gather relevant data, such as retailers for data of human shopping behaviors or hospitals for medical data. Many early-stage startups are solving their cold start problem by creating data simulators to generate contextually relevant data with quality labels in order to train their algorithms.  ... "

P&G Uses SmartLabel Platform

P&G is leader in using means to get to details about thousands of their products.

P&G using technology to peel back curtain on thousands of products on the shelf   By Andy Brownfield  – Reporter, Cincinnati Business Courier

Cincinnati-based consumer goods giant Procter & Gamble Co. is giving consumers an easier way to get insight into thousands of its products using new technology.

Procter & Gamble (NYSE: PG) announced Monday that more than 3,500 of its products are using SmartLabel, a platform that gives consumers on their smartphones or computers detailed information on products, such as ingredients, use instructions, certifications and endorsements. According to a news release, P&G now has more items across more categories on the SmartLabel platform than any other consumer product goods company.

The SmartLabel platform works like this:

Monday, May 21, 2018

Hacking Back: An Active Defense

Interesting thought, but am not sure I would want to get into the battle with the hackers.  Still may be a place someone will have to go to provide an active defense.  Intriguing thoughts that include both hacking and business process.

Active Defense and 'Hacking Back', A Primer
 By Scott Berinato  in the HBR

In the lead piece in this package, Idaho National Lab’s Andy Bochman puts forth a provocative idea: that no amount of spending on technology defenses can secure your critical systems or help you keep pace with hackers. To protect your most valuable information, he argues, you need to move beyond so-called cyber hygiene, the necessary but insufficient deployment of security software and network-monitoring processes. ... " 

Sharepoint Virtual Reality

We experimented with similar ideas.  How do you immerse yourself in messy data?   In information architecture.   We never thought of Sharepoint as a place to start, though we were an MS shop with lots of data of many kinds there.   But will the employee be willing to pick up the headgear, and will that add enough of an engagement to make it worth it?    Maybe if it were complex data we need to navigate?  There will be a gallery of templates to start with, lets see where that takes us.

Microsoft turns SharePoint into the simplest VR creation tool yet
SharePoint spaces is like the PowerPoint of Mixed Reality.

By Devindra Hardawar, @devindra in Engadget

Microsoft is sticking with its pragmatic approach to VR with SharePoint spaces, a new addition to its collaboration platform that lets you quickly build and view Mixed Reality experiences. It's a lot like how PowerPoint made it easy for anyone to create business presentations. Sharepoint spaces features templates for things like a gallery of 3D models or 360-degree videos, all of which are viewable in Mixed Reality headsets (or any browser that supports WebVR). While they're certainly not complex virtual environments, they're still immersive enough to be used for employee training, or as a quick virtual catalog for your customers.  .... " 

Acer Ships with Alexa

Seems to hurt Cortana, but Cortana will also be installed on these same machines along with Windows 10.  But as mentioned, Cortana has been poorly marketed, especially as to its value to support particular consumer needs.  Paul Thurott says it well:

Acer announced this morning that it is the first to ship notebook PCs preinstalled with Amazon Alexa. It won’t be the last.

“We’re delighted to work with Acer to bring Alexa to customers in new ways,” Amazon Alexa vice president Steve Rabuchin says. “We believe customers should be able to interact with Alexa wherever they might need her, including from their PCs, in order to take advantage of the simplicity of voice control.”

That says a lot, I think, about one of Microsoft’s most recent failures. After all, Windows 10 PCs already ship with voice control in the form of Cortana. But that is, perhaps, something that many consumers would never even notice: Cortana usage and capabilities lack far behind those of the digital personal assistant market leaders, Amazon Alexa and Google Assistant. And a PC will work as a secondary device, when it comes to voice control, behind smartphones and even smart speakers. ...." 

AI for Smart Houses

The AI we are using today is simplistic, where will it grow?

Deep Learning, Artificial Intelligence Leading the Way to Smart  Houses
In the Baylor Lariat (TX)   By Samantha Amaro

Baylor University researchers are studying deep learning, with a focus on improving medical imaging and advancing the future of truly smart houses that will perform all manual labor for occupants. The research is divided into two categories: distributed deep learning and energy-efficient deep learning. Distributed deep learning involves investigating how to use several local machines to compute different parts of the main neural network, while energy-efficient deep learning focuses on the problem of being able to provide a constant source of energy for continuous projects. The researchers are using deep learning to analyze medical images, including positron emission tomography (PET) scans and computed tomography (CT) scans. The team is also leading a smart home project to determine whether a house can measure a person's overall health; sensors throughout the house would read a person’s biorhythms and send alerts to the home's occupants if needed.  ... "

Microsoft Buys Semantic Machines

Towards more conversational machines.  We spent many years trying to figure out how analytics, systems and machines could better 'understand' the meaning of data.  Now this will be essential to lead to better conversational interaction.  Note the term 'multiturn' exchanges.  Ultimately its all about the intelligent conversation.

Microsoft snaps up Semantic Machines to build out its conversational AI technology  By Duncan Riley in SiliconAngle
Microsoft Corp. Sunday said it has acquired Semantic Machines Inc., a Berkeley, California-based company that has built a conversational artificial intelligence platform that competes with the likes of Google Inc., for an undisclosed sum.

Founded in 2014, Semantic Machines has designed a new, language-independent technology platform that claims to go beyond understanding commands to understanding conversations. Compared with a neurolinguistic programming approach, the company said, it offers a new technology that extracts semantics across “multiturn” natural language exchanges to maintain contextual understanding over time, enabling computers to communicate, collaborate, understand goals and accomplish tasks.

The acquisition for Microsoft is aimed at boosting its existing conversational AI efforts in services such as Microsoft Cognitive, Cortana and the Azure Bot. The technology and the company itself will be used by Microsoft to establish a conversational AI center of excellence in Berkeley “to push forward the boundaries of what is possible in language interfaces.”  ... " 

Replacing Powerpoint with Narratives

Stories are good, when constructed well.   What if you just want the essential and concise points to carry away.  Narrative also has the stronger possibility of Confirmation Bias.  Its a good story, so its real, true?  And the more you construct it with glossy or animated colorful visuals, the more its correct?  Not saying we to not tell a storywell, but bullet points are useful too.  Brilliant?  No, incomplete.

Jeff Bezos Banned PowerPoint in Meetings.  .....
Narrative memos have replaced PowerPoint presentations at Amazon. Here are three reasons why.
By Carmine Gallo ... 

Sunday, May 20, 2018

Smart Diapers Design with Sensors

Used to work at a company that competed in this space.  In Design, manufacturing and marketing.    Will this compete?

Alphabet’s Verily has a “smart diaper“ design that distinguishes pee from poo   Beyond simple moisture detectors, this techy nappy will analyze the latest download.  By Beth Mole in ArsTechnica

Tech companies are always hoping to clear out the competition with their latest wearable. But Alphabet's life sciences division, Verily, is likely expecting a blow-out with this one.

The company, formerly known as Google Life Sciences, has a patent-pending plan for a wirelessly connected “smart diaper” that would not only alert a caregiver when there’s a new “event” but also analyze and identify the fresh download—i.e., is it a number one or number two? The connected, absorbent gadget will sound the alarm via a connected device and potentially an app, which can catalogue and keep a record of events.

Verily is not the first to try to plumb the potential of derrière devices for babies. Many companies have come before with simple to high-tech moisture sensors—from color-changing strips to wireless alarms. But, Verily argues in its patent application, the market is lacking a convenient, affordable, all-in-one design that can differentiate between a wee squirt and a code brown. While both require attention and a change, a festering or explosive diaper bomb often requires more urgency, particularly if a baby is dealing with diaper rash.  ... " 

Crime Matching with GEDMatch

Interesting this has just become apparent, genetic matching starts to work against increasing stored data and matching.  Shows the power of cowdsourced databases.  Other examples?    Technology Review Shows why and how:

Another arrest shows why no one can hide from the genetic detectives
For the second time this year, investigators used a public DNA database to solve a cold case and find a murderer.

The bust: A 55-year-old truck driver, William Talbott, was arrested today in Washington State after being fingered in a 30-year-old double murder.

How they found him: According to Buzzfeed, investigators located Talbott’s family members after uploading old crime scene DNA to GEDMatch, a crowdsourced database that genealogists use to compare DNA and build family trees.  ...  "

It further comes to mind that this is akin to:

 ' ...   "The Selfish Ledger,” was shared internally within Google. The video examines the possibility of a dystopian world where our use of devices such as smartphones creates a sort of digital DNA, which, like physical DNA, could exist within the context of future generations. ..."

More on that and links to the video on my post here.   Will the crimes of the past always match the crimes considered in the future?

Tesla Releases some of its Code

Intriguing, but as you might expect, the autopilot is very technical.   Tesla is not known for releasing its source code.   Hardly directly understandable, but gives you an impression of the complexity involved.  I am still of the school that says you may not want to release all your code secrets.

More overview:
Tesla releases source code for some of its in-car tech ....It's not everything, but it's finally here. ... " 

By Jon Fingas, @jonfingas  in Engadget

Potential of Data Science

Towards a better definition of data science.

Realizing the Potential of Data Science
By Francine Berman, Rob Rutenbar, Brent Hailpern, Henrik Christensen, Susan Davidson, Deborah Estrin, Michael Franklin, Margaret Martonosi, Padma Raghavan, Victoria Stodden, Alexander S. Szalay

Communications of the ACM, Vol. 61 No. 4, Pages 67-72   10.1145/3188721

The ability to manipulate and understand data is increasingly critical to discovery and innovation. As a result, we see the emergence of a new field—data science—that focuses on the processes and systems that enable us to extract knowledge or insight from data in various forms and translate it into action. In practice, data science has evolved as an interdisciplinary field that integrates approaches from such data-analysis fields as statistics, data mining, and predictive analytics and incorporates advances in scalable computing and data management. But as a discipline, data science is only in its infancy.

The challenge of developing data science in a way that achieves its full potential raises important questions for the research and education community: How can we evolve the field of data science so it supports the increasing role of data in all spheres? How do we train a workforce of professionals who can use data to its best advantage? What should we teach them? What can government agencies do to help maximize the potential of data science to drive discovery and address current and future needs for a workforce with data science expertise? Convened by the Computer and Information Science and Engineering (CISE) Directorate of the U.S. National Science Foundation as a Working Group on the Emergence of Data Science (https://www.nsf.gov/dir/index.jsp?org=CISE), we present a perspective on these questions with a particular focus on the challenges and opportunities for R&D agencies to support and nurture the growth and impact of data science. For the full report on which this article is based, see Berman et al.2

The importance and opportunities inherent in data science are clear (see http://cra.org/data-science/). If the National Science Foundation, working with other agencies, foundations, and industry can help foster the evolution and development of data science and data scientists over the next decade, our research community will be better able to meet the potential of data science to drive new discovery and innovation and help transform the information age into the knowledge age. We hope this article serves as a basis for dialogue within the academic community, the industrial research community, and ACM and relevant ACM special interest groups (such as SIGKDD and SIGHPC).  ... '

Gartner on the Value of AI: $1.2 Trillion

Such valuations are always difficult.  But it can be expected to be high if it truly augments human effort.

Artificial intelligence will be worth $1.2 trillion to the enterprise in 2018
Gartner says that AI-based customer experience technologies are boosting market value.  By Charlie Osborne for Between the Lines

The artificial intelligence (AI) industry will be worth $1.2 trillion in 2018, with customer experience solutions creating the most business value.

On Wednesday, Gartner released estimates on the projected value of AI over the course of this year. According to the research firm, the global enterprise value derived from AI will total $1.2 trillion this year, a 70 percent increase from 2017.

AI-derived business value is projected to reach up to $3.9 trillion by 2022.

"AI promises to be the most disruptive class of technologies during the next 10 years due to advances in computational power, volume, velocity and variety of data, as well as advances in deep neural networks (DNNs)," said John-David Lovelock, research vice president at Gartner. "One of the biggest aggregate sources for AI-enhanced products and services acquired by organizations between 2017 and 2022 will be niche solutions that address one need very well."

These sort of needs may include methods to improve customer experiences, ways to drive new revenue streams, and means to reduce costs, whether operational or in serving existing products. .... "

Blockchain Definitions

A thoughtful set of definitions:

What is a blockchain?
Unpacking the complexity of blockchain, term by term.

By Mike Loukides May 17, 2018

Read "What are Enterprise Blockchains?" and learn how organizations are applying blockchain technology.

So, what is a blockchain? It's a complicated question because the inventor of Bitcoin, the pseudonymous Satoshi Nakamoto, didn't use the term in the original Bitcoin paper. For many, “the blockchain” is nothing more than a shorthand for "how Bitcoin works." But more usefully, the blockchain is a distributed ledger, shared by untrusted participants, with strong guarantees about accuracy and consistency. What does that mean? Let's unpack it term by term: .... " 

Judea Pearl Criticizes Machine Learning

Quite interesting view.  Pearl's view is interesting.  Bayesian networks in particular has shown a more broadly insightful and transparent view to modeling than machine learning.    But machine learning deep learning can target narrower problems more specifically.

How a Pioneer of Machine Learning Became One of Its Sharpest Critics
Judea Pearl helped artificial intelligence gain a strong grasp on probability, but laments that it still can't compute cause and effect.

 By Kevin Hartnett in The Atlantic

Artificial intelligence owes a lot of its smarts to Judea Pearl. In the 1980s he led efforts that allowed machines to reason probabilistically. Now he’s one of the field’s sharpest critics. In his latest book, The Book of Why: The New Science of Cause and Effect, he argues that artificial intelligence has been handicapped by an incomplete understanding of what intelligence really is.

Three decades ago, a prime challenge in artificial-intelligence research was to program machines to associate a potential cause to a set of observable conditions. Pearl figured out how to do that using a scheme called Bayesian networks. Bayesian networks made it practical for machines to say that, given a patient who returned from Africa with a fever and body aches, the most likely explanation was malaria. In 2011 Pearl won the Turing Award, computer science’s highest honor, in large part for this work.

But as Pearl sees it, the field of AI got mired in probabilistic associations. These days, headlines tout the latest breakthroughs in machine learning and neural networks. We read about computers that can master ancient games and drive cars. Pearl is underwhelmed. As he sees it, the state of the art in artificial intelligence today is merely a souped-up version of what machines could already do a generation ago: find hidden regularities in a large set of data. “All the impressive achievements of deep learning amount to just curve fitting,” he said recently.  .... " 

AT&T Builds a Dash-Like Button

Always thought there was a place for simple buttons to link to IOT networks, to make requests of many kinds, beyond just ordering something.   Why doesn't IFTTT have something like this?  This seems to be that,    The way I read it,  although provided through AWS,  it is not an Amazon Echo infrastructure thing.  Will be interesting if that  changes.

AT&T's Dash-like smart button doesn't need WiFi
It's also not pre-programmed like Amazon's one-click device.
By Mariella Moon, @mariella_moon in Engadget

AT&T has launched a new product called LTE-M button, which allows users to place an order online in one click. Yes, it sounds just like Amazon Dash -- in fact, it's powered by Amazon Web Services -- but since it's connected to AT&T's LTE-M network, it doesn't need a WiFi connection to work. AT&T's button was also designed more for businesses than homes and individuals. It's not pre-programmed like Amazon's Dash buttons are, and companies can program it to accomplish tasks that fit their needs. .... "

More details from AT&T

Saturday, May 19, 2018

Business of Artificial Intelligence

 Good statement of expectations, what it is doing, what has been promised, delivered and not,  and where it needs to go.  As a practitioner, I have seen it all to date. ...  It needs care,  caution and closer links to business needs to make it feasible.

Business of AI

By Erik Brynjolfsson and Andrew Mcafee in the HBR

Erik Brynjolfsson (@erikbryn) is the director of MIT’s Initiative on the Digital Economy, the Schussel Family Professor of Management Science at the MIT Sloan School of Management, and a research associate at NBER. His research examines the effects of information technologies on business strategy, productivity and performance, digital commerce, and intangible assets. At MIT he teaches courses on the economics of information and the Analytics Lab.

The Business of Artificial Intelligence

What it can — and cannot — do for your organization

In the sphere of business, AI is poised have a transformational impact, on the scale of earlier general-purpose technologies. Although it is already in use in thousands of companies around the world, most big opportunities have not yet been tapped. The effects of AI will be magnified in the coming decade, as manufacturing, retailing, transportation, finance, health care, law, advertising, insurance, entertainment, education, and virtually every other industry transform their core processes and business models to take advantage of machine learning. The bottleneck now is in management, implementation, and business imagination. ....

Like so many other new technologies, however, AI has generated lots of unrealistic expectations. We see business plans liberally sprinkled with references to machine learning, neural nets, and other forms of the technology, with little connection to its real capabilities. Simply calling a dating site “AI-powered,” for example, doesn’t make it any more effective, but it might help with fundraising. This article will cut through the noise to describe the real potential of AI, its practical implications, and the barriers to its adoption.  ...." 

How to be a Systems Thinker

Podcast video interview.   Its a good idea to create deep understanding.  But shallower understanding sometimes leads to things that are useful,  there has been quite a long history of engineering to show that.  Thoughtful piece,

How To Be a Systems Thinker
A Conversation With Mary Catherine Bateson [4.17.18]

Until fairly recently, artificial intelligence didn’t learn. To create a machine that learns to think more efficiently was a big challenge. In the same sense, one of the things that I wonder about is how we'll be able to teach a machine to know what it doesn’t know that it might need to know in order to address a particular issue productively and insightfully. This is a huge problem for human beings. It takes a while for us to learn to solve problems, and then it takes even longer for us to realize what we don’t know that we would need to know to solve a particular problem.

The tragedy of the cybernetic revolution, which had two phases, the computer science side and the systems theory side, has been the neglect of the systems theory side of it. We chose marketable gadgets in preference to a deeper understanding of the world we live in.

MARY CATHERINE BATESON is a writer and cultural anthropologist. In 2004 she retired from her position as Clarence J. Robinson Professor in Anthropology and English at George Mason University, and is now Professor Emerita. Mary Catherine Bateson's Edge Bio  ... "

OpenAI Lets Robots Learn from Hindsight

In IEEE Spectrum.

OpenAI Releases Algorithm That Helps Robots Learn from Hindsight

It's not a failure if you just pretend that you meant to do it all along  By Evan Ackerman

Being able to learn from mistakes is a powerful ability that humans (being mistake-prone) take advantage of all the time. Even if we screw something up that we’re trying to do, we probably got parts of it at least a little bit correct, and we can build off of the things that we did not to do better next time. Eventually, we succeed.

Robots can use similar trial-and-error techniques to learn new tasks. With reinforcement learning, a robot tries different ways of doing a thing, and gets rewarded whenever an attempt helps it to get closer to the goal. Based on the reinforcement provided by that reward, the robot tries more of those same sorts of things until it succeeds. ... "