Tag Archives: AWS

Unlikely Bedfellows as Net Neutrality Sunsets

December 24, 2017 Ian Roseman Leave a comment

Coupling Content Distribution (i.e. ISPs) with Content Producers

Verizon FiOS offers Netflix as another channel in their already expansive lineup of content. Is this a deal of convenience for the consumer, keeping consumers going through one medium, or is it something more? Amazon Video iOS application offers HBO, STARZ, and others as long as Amazon Prime customers have a subscription to the Content Producers. Convenience or more? The Netflix Content and Distribution via Set-top box (STB) channel should be mimicked by Google YouTube and Amazon Video despite their competing hardware offerings. Consumers should be empowered to decide how they want to consume Amazon Video; e.g. through their Set-top box (STB). However, there may be more than just a convenience benefit.

As Net Neutrality fades into the sunset of congressional debates and lobbyists, the new FCC ruling indicates the prevailing winds of change. We question how content providers, large and small, navigate the path to survival/sustainability. Some business models from content distribution invoke Bandwidth Throttling, which may inhibit the consumers of some content, either by content types (e.g. Video formats) or content providers (e.g. Verizon FiOS providing priority bandwidth to Netflix).

Content Creators / Producers, without a deal with ISPs for “priority bandwidth” may find their customers flock to ‘larger content creators’ who may be able to get better deals for content throughput.

Akamai and Amazon CloudFront – Content Delivery Networks (CDNs)

Content Delivery Networks (CDNs) may find themselves on the better end of this deal, almost as a side-effect to the FCC decision of nixing Net Neutrality.

Amazon CloudFront a global content delivery network (CDN) service that securely delivers data, videos, applications, and APIs to viewers with low latency and high transfer speeds. CloudFront, like Akamai, may significantly benefit from the decision by the FCC to repeal Net Neutrality.

Akamai’s industry-leading scale and resiliency mean delivering critical content with consistency, quality, and security across every device, every time. Great web and mobile experiences are key to engaging users, yet difficult to achieve. To drive engagement and online revenue, it’s critical to optimize performance for consumer audiences and employees alike to meet or exceed their expectations for consistent, fast, secure experiences.

Integrating into Content/Internet Service Provider’s Bundle of Channels

By elevating Content Producers into the ISP (distribution channel) Set-top box (STB), does this ‘packaging’ go beyond bundling of content for convenience? For example, when Netflix uses Verizon FiOS’ CDN for content delivery to their clients, will the consumer benefit from this bundled partnership beyond convenience (i.e. performance)? When Netflix is invoked by a Verizon FiOS customer from their laptop (direct from Netflix), is there a performance improvement if Netflix is invoked from the Verizon FiOS Set-top Box (STB) instead? Would these two separate use cases for invoking Netflix movies utilize two alternate Content delivery network (CDN) paths, one more optimized than the other?

As of this post update (12/26), there has been no comment from Verizon.

Jobs, Recruiting

Uncommon Opportunity? R&D Conversational AI Engineer

June 24, 2017 Ian Roseman Leave a comment

I had to share this opportunity. The Conversational AI Engineer role will continue to be in demand for some time.

Title: R&D Conversational AI Engineer
Location: Englewood Cliffs, NJ
Duration: 6+ months Contract(with Possible extension)

Responsibilities:

Create Alexa Skills, Google Home Actions, and chatbots for various direct Client’s brands and initiatives.
Work with the Digital Enterprises group to create production-ready conversational agents to help Client emerge in the connected life space.
Create additional add-ons to the conversational agents
Work with new technologies not be fully documented yet
Work with startups and their technology emerging in the connected life space.

Quals–
Client is looking for a developer in conversational AI and bot development.

What is Media Labs? Media Labs is dedicated to driving a collaborative culture of innovation across all of Clients . We serve as an internal incubator and accelerator for emerging technology and are leading the way with fresh ideas to ignite the future of media and storytelling. We are committed to partnering with another telecom giant, startups, research and academic groups, content creators and brands to further innovation at client. One of our main themes is connected life and we are looking for an engineer to lead this development.

Requirements for R&D Engineer: –

Bachelor in Computer Science, Engineering, or other related field
Experience working with new technologies that may not be fully documented yet
Experience communicating technology to non-technical people
Experience with AWS (Lambda, CloudWatch, S3, API Gateway, etc)
Experience with JavaScript, Node.js
Some experience creating Alexa Skills, Google Home Actions, or chatbots

Optional Requirements:

Experience creating iOS or Android applications (native or non-native)
Experience with API.AI or another NLP engine (Lex, Watson Conversation)

AI, Technology

Amazon and Microsoft Drinking their own AI Chatbot Champagne?

June 4, 2017 Ian Roseman Leave a comment

A relatively new medium of support for businesses small to global conglomerates becomes available based on the exciting yet embryonic [Chabot] / Digital Agent services. Amazon and Microsoft, among others, are diving into this transforming space. The coat of paint is still wet on Amazon Lex and Microsoft Cortana Skills. MSFT Cortana Skills Kit is not yet available to any/all developers, but has been opened to a select set of partners, enabling them to expand Cortana’s core knowledge set. Microsoft’s Bot Framework is in “Preview” phase. However, the possibilities are extensive, such as another tier of support for both of these companies, if they turn on their own knowledge repositories using their respective Digital Agents [Chabot] platforms.

Approach from Inception to Deployment

The curation and creation of knowledge content may occur with the definition of ‘Goals/Intents’ and their correlated human utterances which trigger the Goal Question and Answer (Q&A) dialog format. Classic Use Case. The question may provide an answer with text, images, and video.
Taking Goals/Intents and Utterances to ‘the next level’ involves creating / implementing Process Workflows (PW). A workflow may contain many possibilities for the user to reach their goal with a single utterance triggered. Workflows look very similar to what you might see in a Visio diagram, with multiple logical paths. Instead of presenting users with the answer based upon the single human utterance, the question, the workflow navigates the users through a narrative to:
- disambiguate the initial human utterance, and get a better understanding of the specific user goal/intention. The user’s question to the Digital Agent may have a degree of ambiguity, and workflows enable the AI Digital Agent to determine the goal through an interactive dialog/inspection. The larger the volume of knowledge, and the closer the goals/intentions, the implementation would require disambiguation.
- interactive conversation / dialog with the AI Digital Agent, to walk through a process step by step, including text, images, and Video inline with the conversation. The AI chat agent may pause the ‘directions’ waiting for the human counterpart to proceed.

Future Opportunities:

Amazon to provide billing and implementation / technical support for AWS services through a customized version of their own AWS Lex service? All the code used to provide this Digital Agent / Chabot maybe ‘open source’ for those looking to implement similar [enterprise] services.
Digital Agent may allow the user to share their screen, OCR the current section of code from an IDE, and perform a code review on the functions / methods.
Microsoft has an ‘Online Chat’ capability for MSDN. Not sure how extensive the capability is, and if its a true 1:1 chat, which they claim is a 24/7 service. Microsoft has libraries of content from Microsoft Docs, MSDN, and TechNet. If the MSFT Bot framework has the capability to ingest their own articles, users may be able to trigger these goals/intents from utterances, similar to searching for knowledge base articles today.
Abstraction, Abstraction, Abstraction. These AI Chatbot/Digital Agents must float toward Wizards to build and deploy, and attempt to stay away from coding. Elevating this technology to be configurable by a business user. Solutions have significant possibilities for small companies, and this technology needs to reach their hands. It seems that Amazon Lex is well on their way to achieving the wizard driven creation / distribution, but have ways to go. I’m not sure if the back end process execution, e.g. Amazon Lambda, will be abstracted any time soon.

Technology

Evaluating Amazon Lex – AI Digital Agent / Assistant Implementation

June 1, 2017 Ian Roseman Leave a comment

Evaluating AI chatbot solutions for:

Simple to Configure – e.g. Wizard Walkthrough
Flexible, and Mature Platform e.g. Executing backend processes
Cost Effective and Competitive Solutions
Rapid Deployment to XYZ platforms

The idea is almost anyone can build and deploy a chat bot for your business, small to midsize organizations.

Amazon Lex

Going through the Amazon Lex build chat process, and configuration of the Digital Assistant was a breeze. AWS employs a ‘wizard’ style interface to help the user build the Chatbot / Digital Agent. The wizard guides you through defining Intents, Utterances, Slots, and Fulfillment.

Intents – A particular goal that the user wants to achieve (e.g. book an airline reservation)
Utterances – Spoken or typed phrases that invoke your intent
Slots – Data the user must provide to fulfill the intent
Prompts – Questions that ask the user to input data
Fulfillment – The business logic required to fulfill the user’s intent (i.e. backend call to another system, e.g. SAP)

The Amazon Lex Chatbot editor is also extremely easy to use, and to update / republish any changes.

The challenge with Amazon Lex appears to be a very limiting ability for chatbot distribution / deployment. Your Amazon Lex Chatbot is required to use one of three methods to deploy: Facebook, Slack, or Twilio SMS. Facebook is limiting in a sense if you do not want to engage your customers on this platform. Slack is a ‘closed’ framework, whereby the user of the chat bot must belong to a Slack team in order to communicate. Finally, Twilio SMS implies use of your chat bot though a mobile phone SMS.

I’ve reached out to AWS Support regarding any other options for Amazon Lex chatbot deployment. Just in case I missed something.

There is a “Test Bot” in the lower right corner of the Amazon Lex, Intents menu. The author of the business process can, in real-time, make changes to the bot, and test them all on the same page.

Key Followups

Is there a way to leverage the “Test Bot” as a “no frills” Chatbot UI, and embed it in an existing web page? Question to AWS Support.
One concern is for large volumes of utterances / Intents and slots. An ideal suggestion would allow the user a bulk upload through an Excel spreadsheet, for example.
I’ve not been able to utilize the Amazon Lambda to trigger server side processing.
Note: there seem to be several ‘quirky’ bugs in the Amazon Lex UI, so it may take one or two tries to workaround the UI issue.

IBM Watson Conversation also contends for this Digital Agent / Assistant space, and have a very interesting offering including dialog / workflow definition.

Both Amazon Lex and IBM Watson Conversation are FREE to try, and in minutes, you could have your bots created and deployed. Please see sites for pricing details.

Business, Development, Education, Entertainment, Legal, Media, Music, Technology, Work

Cloud Storage: Ingestion, Management, and Sharing

June 23, 2016 Ian Roseman Leave a comment

Cloud Storage Solutions need differentiation that matters, a tipping point to select one platform over the other.

Common Platforms Used:

Differentiation may come in the form of:

Collaborative Content Creation Software, such as DropBox Paper enables individuals or teams to produce content, all the while leveraging the Storage platform for e.g. version control,
Embedded integration in a suite of content creation applications, such as Microsoft Office, and OneDrive.
Making the storage solution available to developers, such as with AWS S3, and Box. Developers may create apps powered by the Box Platform or custom integrations with Box
iCloud enables users to backup their smartphone, as well tightly integrating with the capture and sharing of content, e.g. Photos.

Cloud Content Lifecycle Categories:

Content Creation
- 3rd Party (e.g. Camera) or Integrated Platform Products
Content Ingestion
- Capture Content and Associated Metadata
Content Collaboration
- Share, Update and Distribution
Content Discovery
- Surface Content; Searching and Drill Down
Retention Rules
- Auto expire pointer to content, or underlying content

Cloud Content Ingestion Services:

Business, Development, Economy, Entertainment, Finance, Law, Media, Mobility, Technology, Work

Time Lock Access: Seal Files in Cloud Storage

May 1, 2016 Ian Roseman 1 Comment

Is there value in providing users the ability to apply “Time Lock Access” to files in cloud storage? Files are securely uploaded by their Owner. After upload no one, including the Owner, may access / open the file(s). Only after the date and time provided for the time lock passes, files will be available for access, and action may be taken, e.g. Automatically email a link to the files. More complex actions may be attached to the time lock release such as script execution using a simple set of rules as defined by the file Owner.

Solution already exists? Please send me a link to the cloud integration product / plug in.

Advertising, Business, Child Development, Development, Economy, Education, Entertainment, Finance, Help, Mobility, Retail, Technology

AI Personal Assistants are “Life Partners”

April 21, 2016 Ian Roseman Leave a comment

Artificial Intelligent (AI) “Assistants”, or “Bots” are taken to the ‘next level’ when the assistant becomes a proactive entity based on the input from human intelligent experts that grows with machine learning.

Even the implication of an ‘Assistant’ v. ‘Life Partner’ implies a greater degree of dynamic, and proactive interaction. The cross over to becoming ‘Life Partner’ is when we go ‘above and beyond’ to help our partners succeed, or even survive the day to day.

Once we experience our current [digital, mobile] ‘assistants’ positively influencing our lives in a more intelligent, proactive manner, an emotional bond ‘grows’, and the investment in this technology will also expand.

Practical Applications Range:

Alcoholics Anonymous Coach , Mentor – enabling the human partner to overcome temporary weakness. Knowledge, and “triggers” need to be incorporated into the AI ‘Partner’; “Location / Proximity” reminder if person enters a shopping area that has a liquor store. [AI] “Partner” help “talk down”
Understanding ‘data points’ from multiple sources, such as alarms, and calendar events, to derive ‘knowledge’, and create an actionable trigger.
- e.g. “Did you remember to take your medicine?” unprompted; “There is a new article in N periodical, that pertains to your medicine. Would you like to read it?”
- e.g. 2 unprompted, “Weather calls for N inches of Snow. Did you remember to service your Snow Blower this season?”
FinTech – while in department store XYZ looking to purchase Y over a certain amount, unprompted “Your credit score indicates you are ‘most likely’ eligible to ‘sign up’ for a store credit card, and get N percentage off your first purchase” Multiple input sources used to achieve a potential sales opportunity.

IBM has a cognitive cloud of AI solutions leveraging IBM’s Watson. Most/All of the 18 web applications they have hosted (with source) are driven by human interactive triggers, as with the “Natural Language Classifier”, which helps build a question-and-answer repository.

There are four bits that need to occur to accelerate adoption of the ‘AI Life Partner’:

Knowledge Experts, or Subject Matter Experts (SME) need to be able to “pass on” their knowledge to build repositories. IBM Watson Natural Language Classifier may be used.
The integration of this knowledge into an AI medium, such as a ‘Digital Assistant’ needs to occur with corresponding ‘triggers’
Our current AI ‘Assistants’ need to become [more] proactive as they integrate into our ‘digital’ lives, such as going beyond the setting of an alarm clock, hands free calling, or checking the sports score. Our [AI] “Life Partner” needs to ‘act’ like buddy and fan of ‘our’ sports team. Without prompting, proactively serve up knowledge [based on correlated, multiple sources], and/or take [acceptable] actions.
1. E.g. FinTech – “Our schedule is open tonight, and there are great seats available, Section N, Seat A for ABC dollars on Stubhub. Shall I make the purchase?”
  1. Partner with vendors to drive FinTech business rules.
Take ‘advantage’ of more knowledge sources, such as the applications we use that collect our data. Use multiple knowledge sources in concert, enabling the AI to correlate data and propose ‘complex’ rules of interaction.

Our AI ‘Life Partners’ may grow in knowledge, and mature the relationship between man and machine. Incorporating derived rules leveraging machine learning, without input of a human expert, will come with risk and reward.

Advertising, Business, Development, Economy, Education, Entertainment, Fashion, Finance, Fun, Law, Retail, Science, Technology

The Race Is On to Control Artificial Intelligence, and Tech’s Future

March 25, 2016 Ian Roseman Leave a comment

Amazon, Google, IBM and Microsoft are using high salaries and games pitting humans against computers to try to claim the standard on which all companies will build their A.I. technology.

In this fight — no doubt in its early stages — the big tech companies are engaged in tit-for-tat publicity stunts, circling the same start-ups that could provide the technology pieces they are missing and, perhaps most important, trying to hire the same brains.

For years, tech companies have used man-versus-machine competitions to show they are making progress on A.I. In 1997, an IBM computer beat the chess champion Garry Kasparov. Five years ago, IBM went even further when its Watson system won a three-day match on the television trivia show “Jeopardy!” Today, Watson is the centerpiece of IBM’s A.I. efforts.

Today, only about 1 percent of all software apps have A.I. features, IDC estimates. By 2018, IDC predicts, at least 50 percent of developers will include A.I. features in what they create.

Source: The Race Is On to Control Artificial Intelligence, and Tech’s Future – The New York Times

The next “tit-for-tat” publicity stunt should most definitely be a battle with robots, exactly like BattleBots, except…

Use A.I. to consume vast amounts of video footage from previous bot battles, while identifying key elements of bot design that gave a bot the ‘upper hand’. From a human cognition perspective, this exercise may be subjective. The BattleBot scoring process can play a factor in 1) conceiving designs, and 2) defining ‘rules’ of engagement.
Use A.I. to produce BattleBot designs for humans to assemble.
Autonomous battles, bot on bot, based on Artificial Intelligence battle ‘rules’ acquired from the input and analysis of video footage.

Roseman Solutions LLC