Are you ready for a challenge, and 150,000 USD to begin to pursue your challenge?
That’s just SBIR Phase I, Concept Development (~6 months). The second phase, Prototype Development, may be funded up to 1 MM USD, and last 24 months.
The Small Business Innovation Research (SBIR) program is a highly competitive program that encourages domestic small businesses to engage in Federal Research/Research and Development (R/R&D) that has the potential for commercialization. Through a competitive awards-based program, SBIR enables small businesses to explore their technological potential and provides the incentive to profit from its commercialization. By including qualified small businesses in the nation’s R&D arena, high-tech innovation is stimulated and the United States gains entrepreneurial spirit as it meets its specific research and development needs.
The program’s goals are four-fold:
Stimulate technological innovation.
Meet Federal research and development needs.
Foster and encourage participation in innovation and entrepreneurship by socially and economically disadvantaged persons.
Increase private-sector commercialization of innovations derived from Federal research and development funding.
It seems that car manufacturers, among others, are building autonomous hardware (i.e. vehicle and other sensors) as well as the software to govern their usage. Few companies are separating the hardware and software layers to explicitly carve out the autonomous software, for example.
Yes, there are benefits to tightly couple the autonomous hardware and software:
1. Proprietary implementations and intellectual property – Implementing autonomous vehicles within a single corporate entity may ‘fast track’ patents, and mitigate NDA challenges / risks
2. Synergies with two (or more) teams working in unison to implement functional goals. However, this may also be accomplished through two organizations with tightly coupled teams. Engaged, strong team leadership to help eliminate corp to corp BLOCKERS, must be in place to ensure deliverables.
There are also advantages with two separate organizations, one the software layer, and the other, the vehicle hardware implementation, i.e. sensors
1. Implementation of Autonomous Vehicle Hardware from AI Software enables multiple, strong alternate corporate perspectives These perspectives allow for a stronger, yet balanced approach to implementation.
2. The AI Software for Autonomous vehicles, if contractually allowed, may work with multiple brand vehicles, implementing similar capabilities. Vehicles now have capabilities / innovations shared across the car industry. The AI Software may even become a standard in implementing Autonomous vehicles across the industry.
3. Working with multiple hardware / vehicle manufactures may allow the enablement of Software APIs, layer of implementation abstraction. These APIs may enable similar approaches to implementation, and reduce redundancy and work can be used as ‘the gold standard’ in the industry.
4. We see commercial adoption of autonomous vehicle features such as “Auto Lane Change”, and “Automatic Emergency Braking.” so it makes sense to adopt standards through 3rd Party AI software Integrators / Vendors
5. Incorporating Checks and Balances to instill quality into the product and the process that governs it.
In summation, Car parts are typically not built in one geographic location, but through a global collaboration. Autonomous software for vehicles should be externalized in order to overcome unbiased safety and security requirements. A standards organization “with teeth” could orchestrate input from the industry, and collectively devise “best practices” for autonomous vehicles.
I remember building a companion app for the Windows desktop that pulled music data from iTunes and Gracenote. Gracenote boasts:
“Gracenote technology is at the heart of every great entertainment experience, and is supported by the largest source of music metadata on the planet..”
Gracenote, in conjunction with the iTunes API / data allowed me to personalize the user experience beyond what iTunes provided out of the box. X-Ray IMDb on Amazon Video also enriches the experience of watching movies and television hosted on Amazon Video .
While watching a movie using Amazon Video, you can tap the screen, and get details about the specific scene, shown in the foreground as the media continues to play.
“Go behind the scenes of your favorite movies and TV shows with X-Ray, powered by IMDb. Get instant access to cast photos, bios, and filmographies, soundtrack info, and trivia. “
IMDb is an Amazon company, which in his infinite foresight, in 1998, Jeff Bezos, founder, owner and CEO of Amazon.com, struck a deal to buy IMDb outright for approximately $55 million and attach it to Amazon as a subsidiary, private company.
The Internet Movie Database (abbreviated IMDb) is an online database of information related to films, television programs and video games, including cast, production crew, fictional characters, biographies, plot summaries, trivia and reviews, operated by IMDb.com, Inc., a subsidiary of Amazon. As of June 2017, IMDb has approximately 4.4 million titles (including episodes), 8 million personalities in its database, as well as 75 million registered users.
In Amazon’s infinite wisdom again, they are looking to stretch both X-Ray and the IMDb property to budding film artists looking to cultivate and mature their following.
Manage your photos and the credits you are Known For on IMDbPro, IMDb, and Amazon Video”
How then is new media content, such as Actor’s photos, and Filmography [approved] and updated by IMDb.
Furthermore, what is the selection process to get indie content [approved] and posted to Amazon video. Is there a curation process whereby not every indie artist is hosted, e.g. creative selection process is driven by Amazon Video business.
To expand the use of X-Ray powered by IMDb, what are the options for alternate Media Players and Streamers? e.g. is YouTube a possibility, hosting and streaming content embedded with X-Ray capabilities? Does Amazon X-Ray enabled capabilities require the Amazon Video player?
X-Ray Current Support: Amazon Hosted and Streaming
X-Ray is available on the Amazon Video appin the US, UK, Germany, and Austria for thousands of titles on compatible devices including Amazon Fire Tablets and TV/Stick, iOS and Android mobile devices, and the web. To access X-Ray, tap the screen or click on the Fire TV remote while the video is playing.”
Amazon X-Ray Studios, Video Editing/Integration Desktop Application
Indie producers may leverage X-Ray Studios to integrate IMDb overlay content to enhance their audience’s experience. Timecodes are leveraged to sync up X-Ray content with the video content.
Are you planning on traveling this week for your vacation? Bought a gas guzzler instead of an EV because of the time it takes to charge, and chargers were not readily available on your commute or a road trip? Don’t want to get stuck without a charge, or kill a half day recharging your wheels? It looks like with the release of the Tesla Model 3 it will address most of these items such as:
Tesla Supercharger provides up to 170 miles of range in as little as 30 minutes.
Relatively Affordable – 35k
But how wide spread are the charging stations, and what could be done as a catalyst to rapidly improve EV charging coverage? We can look at EV charging coverage, US map like we do for cellular coverage (and range). One possibility is to tack on a rider to federal funding requiring states that use federal funding for their highways implement EV Charging Stations at EVERY rest stop along the road.
However, there are a vast array of EV Charging Stations on the market. Although adoption across the US is currently sporadic, there are several brands/standards of EV Charging Mechanisms
A heterogenous mix of N EV chargers per rest stop / station, may allow for a diverse set of solutions.
Each state may declare N companies to install, maintain, and periodic upgrades for each EV Charging Station. Each rest stop / station can be awarded to separate contractors, or clustered
The ‘bid winning’ companies per state responsible for the rest station EV chargers must perform ongoing evaluations of EV charging station adoption, and use quantifiable data for upgrading solutions, after N period of time.
Stakeholders to Gain from Expansion of EV Charging Stations across the United States of America.
Finally, is there really a “lack” of EV Charging Stations across the United States? Here is a map from the U.S. Department of Energy National Renewable Energy Laboratory’s (NREL) list of charging stations.
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)
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.
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)
Some experience creating Alexa Skills, Google Home Actions, or chatbots
Experience creating iOS or Android applications (native or non-native)
Experience with API.AI or another NLP engine (Lex, Watson Conversation)
Amazon’s Echo and Google’s Home are the two most compelling products in the new smart-speaker market. It’s a fascinating space to watch, for it is of substantial strategic importance to both companies as well as several more that will enter the fray soon. Why is this? Whatever device you outfit your home with will influence many downstream purchasing decisions, from automation hardware to digital media and even to where you order dog food. Because of this strategic importance, the leading players are investing vast amounts of money to make their product the market leader.
These devices have a broad range of functionality, most of which is not discussed in this article. As such, it is a review not of the devices overall, but rather simply their function as answer engines. You can, on a whim, ask them almost any question and they will try to answer it. I have both devices on my desk, and almost immediately I noticed something very puzzling: They often give different answers to the same questions. Not opinion questions, you understand, but factual questions, the kinds of things you would expect them to be in full agreement on, such as the number of seconds in a year.
As someone who has worked with Artificial Intelligence in some shape or form for the last 20 years, I’d like to throw in my commentary on the article.
Human Utterances and their Correlation to Goal / Intent Recognition. There are innumerable ways to ask for something you want. The ‘ask’ is a ‘human utterance’ which should trigger the ‘goal / intent’ of what knowledge the person is requesting. AI Chat Bots, digital agents, have a table of these utterances which all roll up to a single goal. Hundreds of utterances may be supplied per goal. In fact, Amazon has a service, Mechanical Turk, the Artificial Artificial Intelligence, which you may “Ask workers to complete HITs – Human Intelligence Tasks – and get results using Mechanical Turk”. They boast access to a global, on-demand, 24 x 7 workforce to get thousands of HITs completed in minutes. There are also ways in which the AI Digital Agent may ‘rephrase’ what the AI considers utterances that are closely related. Companies like IBM look toward human recognition, accuracy of comprehension as 95% of the words in a given conversation. On March 7, IBM announced it had become the first to hone in on that benchmark, having achieved a 5.5% error rate.
Algorithmic ‘weighted’ Selection verses Curated Content. It makes sense based on how these two companies ‘grew up’, that Amazon relies on their curated content acquisitions such as Evi, a technology company which specialises in knowledge base and semantic search engine software. Its first product was an answer engine that aimed to directly answer questions on any subject posed in plain English text, which is accomplished using a database of discrete facts. “Google, on the other hand, pulls many of its answers straight from the web. In fact, you know how sometimes you do a search in Google and the answer comes up in snippet form at the top of the results? Well, often Google Assistant simply reads those answers.” Truncated answers equate to incorrect answers.
Instead of a direct Q&A style approach, where a human utterance, question, triggers an intent/goal , a process by which ‘clarifying questions‘ maybe asked by the AI digital agent. A dialog workflow may disambiguate the goal by narrowing down what the user is looking for. This disambiguation process is a part of common technique in human interaction, and is represented in a workflow diagram with logic decision paths. It seems this technique may require human guidance, and prone to bias, error and additional overhead for content curation.
Who are the content curators for knowledge, providing ‘factual’ answers, and/or opinions? Are curators ‘self proclaimed’ Subject Matter Experts (SMEs), people entitled with degrees in History? or IT / business analysts making the content decisions?
Questions requesting opinionated information may vary greatly between AI platform, and between questions within the same AI knowledge base. Opinions may offend, be intentionally biased, sour the AI / human experience.
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.
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.
Interesting approach to an AI Chatbot implementation. The business process owner creates one or more Google Forms containing questions and answers, and converts/deploys to a chatbot using fobi.io. All the questions for [potential] customers/users are captured in a multitude of forms. Without any code, and within minutes, an interactive chatbot can be produced and deployed for client use.
The trade off for rapid deployment and without coding is a rigid approach of triggering user desired “Goal/Intents”. It seems a single goal/intent is mapped to a single Google Form. As opposed to a digital agent, which leverages utterances to trigger the user’s intended goal/intent. Before starting the chat, the user must select the appropriate Google Form, with the guidance of the content curator.
Another trade off is, it seems, no integration on the backend to execute a business process, essential to many chatbot workflows. For example, given an Invoice ID, the chatbot may search in a transactional database, then retrieve and display the full invoice. Actually, I may be incorrect. On the Google Forms side, there is a Script Editor. Seems powerful and scary all at the same time.
Another trade off that seems to exist, more on the Google Forms side, is building not just a Form with a list of Questions, but a Consumer Process Workflow, that allows the business to provide an interactive dialog based on answers users provide. For example, a Yes/No or multichoice answer may lead to alternate sets of questions [and actions]. It doesn’t appear there is any workflow tool provided to structure the Google Forms / fobi.io chatbot Q&A.
However, there are still many business cases for the product, especially for small to mid size organizations.
* Business Estimates – although there is no logic workflow to guide the Q&A sessions with [prospective] customers, the business still may derive the initial information they require to make an initial assessment. It seems a Web form, and this fobi.io / Google Forms solution seems very comparable in capability, its just a change in the median in which the user interacts to collect the information.
One additional note, Google Forms is not a free product. Looks like it’s a part of the G Suite. Free two week trial, then the basic plan is $5 per month, which comes with other products as well. Click here for pricing details.
Although this “chatbot” tries to quickly provide a mechanism to turn a form to a chatbot, it seems it’s still just a form at the end of the day. I’m interested to see more products from Zoi.ai soon
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.
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.
Google may attempt to leapfrog their Digital Assistant competition by taking advantage of their ability to search against all Google products. The more personal data a Digital Assistant may access, the greater the potential for increased value per conversation.
As a first step, Google’s “Personal” Search tab in their Search UI has access to Google Calendar, Photos, and your Gmail data. No doubt other Google products are coming soon.
Big benefits are not just for the consumer to search through their Personal Goggle data, but provide that consolidated view to the AI Assistant. Does the Google [Digital] Assistant already have access to Google Keep data, for example. Is providing Google’s “Personal” search results a dependency to broadening the Digital Assistant’s access and usage? If so, these…
interactions are most likely based on a reactive model, rather than proactive dialogs, i.e. the Assistant initiating the conversation with the human.
“What you need, before you ask. Stay a step ahead with Now cards about traffic for your commute, news, birthdays, scores and more.”
I’m not sure how proactive the Google AI is built to provide, but most likely, it’s barely scratching the service of what’s possible.
Modeling Personal, AI + Human Interactions
Starting from N number of accessible data sources, searching for actionable data points, correlating these data points to others, and then escalating to the human as a dynamic or predefined Assistant Consumer Workflow (ACW). Proactive, AI Digital Assistant initiates human contact to engage in commerce without otherwise being triggered by the consumer.
Actionable data point correlations can trigger multiple goals in parallel. However, the execution of goal based rules would need to be managed. The consumer doesn’t want to be bombarded with AI Assistant suggestions, but at the same time, “choice” opportunities may be appropriate, as the Google [mobile] App has implemented ‘Cards’ of bite size data, consumable from the UI, at the user’s discretion.
As an ongoing ‘background’ AI / ML process, Digital Assistant ‘server side’ agent may derive correlations between one or more data source records to get a deeper perspective of the person’s life, and potentially be proactive about providing input to the consumer decision making process.
The proactive Google Assistant may suggest to book your annual fishing trip soon. Elevated Interaction to Consumer / User.
The Assistant may search Gmail records referring to an annual fishing trip ‘last year’ in August. AI background server side parameter / profile search. Predefined Assistant Consumer Workflow (ACW) – “Annual Events” Category. Building workflows that are ‘predefined’ for a core set of goals/rules.
AI Assistant may search user’s photo archive on the server side. Any photo metadata could be garnished from search, including date time stamps, abstracted to include ‘Season’ of Year, and other synonym tags.
Photos from around ‘August’ may be earmarked for Assistant use
Photos may be geo tagged, e.g. Lake Champlain, which is known for its fishing.
All objects in the image may be stored as image metadata. Using image object recognition against all photos in the consumer’s repository, goal / rule execution may occur against pictures from last August, the Assistant may identify the “fishing buddies” posing with a huge “Bass fish”.
In addition to the Assistant making the suggestion re: booking the trip, Google’s Assistant may bring up ‘highlighted’ photos from last fishing trip to ‘encourage’ the person to take the trip.
This type of interaction, the Assistant has the ability to proactively ‘coerce’ and influence the human decision making process. Building these interactive models of communication, and the ‘management’ process to govern the AI Assistant is within reach.
Predefined Assistant Consumer / User Workflows (ACW) may be created by third parties, such as Travel Agencies, or by industry groups, such as foods, “low hanging fruit” easy to implement the “time to get more milk” . Or, food may not be the best place to start, i.e. Amazon Dash