Curated content for content, computing, and digital experience professionsals

Tag: AI (Page 3 of 3)

Gilbane Advisor 1-5-17 — Bots, Deep Learning, Mobile Marketing

Happy New year Dear Reader!

We have chosen a small number of the superabundance of end-of-year reviews and predictions to recommend, each focused on rapidly developing areas that are important for you to keep up with, even if at a high level. Topics include bots, deep learning, mobile, marketing technology, software development, and design.

Bot Check-In: A Year of Disappointment

Sam Lessin sums it up and explains.

Despite lots of PR, neither Facebook, Amazon or Google-developed bot platforms this year made it easy for developers to work with. Narrowly focused services can thrive in the near-term but mass-market bots have a way to go. Read More

Deep Learning 2016: The Year in Review

Jan Bussieck provides a really useful, not-too-technical, recap and look forward.

The many revolutionary results we have seen in 2016, be they in medical imaging, self-driving cars or machine translation also point to the fact that moving along the axes of data and compute power will lead to diminishing marginal returns… This means that the greatest yields can be reaped from pushing the third frontier forward, to develop algorithms that can learn from raw unlabelled data such as video or text. Read More

2016’s top programming trends

2016 brought many exciting developments in software and 2017 promises to be even better as containers and functional programming languages grow in adoption and JavaScript moves to become even more central parts of standard development practice. Read More 

Mobile is eating the world – 2016

Benedict Evans’ latest version of his well known presentation is not just about mobile but covers computing and market issues in the context of today’s dominant platform. The link provides access to both his slides and video presentation. Read More

2017 predictions for mobile marketing

In terms of industry headline value, possibly the two most intriguing mobile advertising developments in 2016 were perpetrated by platform operators: Google massively extended its suite of mobile advertising products and Apple introduced in-store search ads…

Eric Seufer provides three predictions for this year. Read More

Who controls the marketing tech stack in 2017: The CIO or CMO?

There are lots of workable options for collaboration. Dion Hinchcliffe suggests some scenarios. Read More

Also…

Keep up with design… Top 10 UX Articles of 2016 Announced via Nielsen Norman Group

And don’t forget… Content Structure in Tables via Story Needle

When Java shops grow up they become web companies or vice versa? via Redmonk

Speaking of software… Is it a good time to start a software company? via Sam Gerstenzang

What happens when everyone has a camera?… Cameras, ecommerce and machine learning via Benedict Evans

 

The Gilbane Advisor curates content for our community of content, computing, and digital experience professionals. Subscribe to our newsletter, or our feed.

Gilbane Advisor 7-26-16 – Bots, apps, stickers, video, web, product placement

In this issue we look at moderating perspectives on a few hot topics. First up, we follow up on our earlier coverage of bots and use cases. There are lots of good reasons to be excited about the growth and range of applications for bots, but the hype has been a bit much. We have chosen four short articles to get you current. Next up, Stickers as a serious content type, then thoughts on the utility of video, purposeful merging of web and mobile, and interesting ideas on product placement.

Wrong Narrative. Wrong Mindset. Wrong Solutions.

Don’t worry, this is about bots not politics, and Sar Haribhakti’s view is balanced.

If you even remotely follow tech news, it is very likely that you have read outlandish claims like “Bots are the new apps”. There are way too many misconceptions around conversational products. Read More

Bots in 2016: Mid-Year Check In

Sam Lessin is a big fan of bots because of the potential for developers in general, and in particular for the personal assistant his company, Fin, is building. His experience and thoughts on the challenges of bringing bot-based products to market will help developers and product managers tune expectations.

The past six months have shown that building compelling bots is very challenging. Completely automated bots aren’t ready for prime time and hybrid services with human involvement cost a lot to develop. How developers can make money from bots also hasn’t been clarified. But I’m still bullish on the shift to bots. Read More

Personal assistant bots like Siri and Cortana have a serious problem

Good general explanation of why Alexa et al struggle to meet user expectations. Read More

The Death of Apps has been Greatly Exaggerated

Analyst Jan Dawson looks at the numbers.

Bots are a fascinating new element of mobile user interfaces and have potential in certain well-defined use cases. However, as a threat to the app model, they’re fundamentally limited for a couple of reasons. First, bots are not a fit at all for the vast majority of apps. Put another way, for the apps that generate the vast majority of revenue. Read More

The Elements of Stickers

Stickers as a serious content type. Connie Chan should convince you there is more to Stickers than you probably thought.

Stickers aren’t just frivolous little punctuation marks to be inserted in text messages. They can be replacements for entire sentences, and help create a new medium for communicating and storytelling… And sometimes stickers can convey what words cannot! This form of visual communication has become so popular … in WeChat and LINE — that it is not uncommon to see a deep thread of multiple messages without a single word. They’re not just for those crazy young kids. More notably, stickers are commonly used in professional, not just personal, chats as well… Read More

Video and Speed

I’m sure you know the feel­ing  —  you see a link to some­thing that looks in­ter­est­ing, fol­low it, and when it turns out to be a video clip, you shake your head and kill the tab. The prob­lem with video is it’s just too slow.

Totally agree. Give me words to skim please. Tim Bray discusses watching (some) video faster and listening to sped-up audio. Read More

Building for a future mobile web

Paul Kinlan makes a case for progressive web apps as a way to combine the best of native mobile and mobile web apps. His discussion is useful beyond Google’s progressive web activity.

A new way of thinking about how we build apps and content experiences for the web is needed. One that is progressive in allowing us to build for the web at large, but takes inspiration from the deeper engagement that can be found in native app platforms. Read More

James Bond, Dunder Mifflin, and the Future of Product Placement

…my children shouted in annoyance. They rousted me from bed, complaining that something was wrong with the TV… Trying to explain that there are advertising breaks so people can sell them things was a deflating experience… Their incomprehension was total… If interruption-based advertising is no longer an option, and if traditional product placement is no longer the answer, how can brands reach consumers while not offending their sense of empowerment and leveraging their desire for immediate gratification?

Laurent Muzellec makes a case for “interactive product placement”, and “reverse product placement”… Read More

 

Gilbane Digital Content Conference
Main conference: November 29 – 30
Workshops: December 1, 2016
Fairmont Copley Plaza, Boston

Short takes

The topic may be slightly scary but you need to know what’s coming… 2 terrific #MarTech talks on the rise of AI in marketing via chiefmartec.com

Lots of orgs will need to make a similar choice, but this is big one… Salesforce will only support Nexus and Samsung Galaxy phones to avoid Android fragmentation via recode

Yes, something like this… Why Wearables Will Replace Your Smartphone via ITNews

eBooks down, print up… U.S. Publishing Industry’s Annual Survey Reveals Nearly $28 Billion in Revenue in 2015 via AAP

An optimist’s view of Getting Google and Publishers to Share Love — and Data via Medium

Differences… AI, Apple and Google via Benedict Evans

The UK takes a stab at a Data Science Ethical Framework via gov.uk

Growing without video dependence… Bustle’s Slower but Steady Growth Path via The Information

Different levels of maturity… Enterprise MarTech Assessment: More Social Than Mobile via Medium

CMS, etc., corner

Should command a serious salary… The 6 skills every content strategist must have via Medium

Some good questions re What does a decoupled WCM architecture really mean? via Real Story Group

Digital Asset Management Round-up, June 2016 via Digital Clarity Group

Bright future or death spiral?…   via EContent

About

The Gilbane Advisor curates content for our community of content, computing, and digital experience professionals. Subscribe to our newsletter, or our feed.

The Gilbane Digital Content Conference: Content Management, Marketing, and Digital Experience helps marketers, IT, and business managers integrate content strategies and technologies to produce superior digital experiences for customers, employees, and partners.

How Far Does Semantic Software Really Go?

A discussion that began with a graduate scholar at George Washington University in November, 2010 about semantic software technologies prompted him to follow up with some questions for clarification from me. With his permission, I am sharing three questions from Evan Faber and the gist of my comments to him. At the heart of the conversation we all need to keep having is, how far does this technology go and does it really bring us any gains in retrieving information?

1. Have AI or semantic software demonstrated any capability to ask new and interesting questions about the relationships among information that they process?

In several recent presentations and the Gilbane Group study on Semantic Software Technologies, I share a simple diagram of the nominal setup for the relationship of content to search and the semantic core, namely a set of terminology rules or terminology with relationships. Semantic search operates best when it focuses on a topical domain of knowledge. The language that defines that domain may range from simple to complex, broad or narrow, deep or shallow. The language may be applied to the task of semantic search from a taxonomy (usually shallow and simple), a set of language rules (numbering thousands to millions) or from an ontology of concepts to a semantic net with millions of terms and relationships among concepts.

The question Evan asks is a good one with a simple answer, “Not without configuration.” The configuration needs human work in two regions:

  • Management of the linguistic rules or ontology
  • Design of search engine indexing and retrieval mechanisms

When a semantic search engine indexes content for natural language retrieval, it looks to the rules or semantic nets to find concepts that match those in the content. When it finds concepts in the content with no equivalent language in the semantic net, it must find a way to understand where the concepts belong in the ontological framework. This discovery process for clarification, disambiguation, contextual relevance, perspective, meaning or tone is best accompanied with an interface making it easy for a human curator or editor to update or expand the ontology. A subject matter expert is required for specialized topics. Through a process of automated indexing that both categorizes and exposes problem areas, the semantic engine becomes a search engine and a questioning engine.

The entire process is highly iterative. In a sense, the software is asking the questions: “What is this?”, “How does it relate to the things we already know about?”, “How is the language being used in this context?” and so on.

2. In other words, once they [the software] have established relationships among data, can they use that finding to proceed – without human intervention- to seek new relationships?

Yes, in the manner described for the previous question. It is important to recognize that the original set of rules, ontologies, or semantic nets that are being applied were crafted by human beings with subject matter expertise. It is unrealistic to think that any team of experts would be able to know or anticipate every use of the human language to codify it in advance for total accuracy. The term AI is, for this reason, a misnomer because the algorithms are not thinking; they are only looking up “known-knowns” and applying them. The art of the software is in recognizing when something cannot be discerned or clearly understood; then the concept (in context) is presented for the expert to “teach” the software what to do with the information.

State-of-the-art software will have a back-end process for enabling implementer/administrators to use the results of search (direct commentary from users or indirectly by analyzing search logs) to discover where language has been misunderstood as evidenced by invalid results. Over time, more passes to update linguistic definitions, grammar rules, and concept relationships will continue to refine and improve the accuracy and comprehensiveness of search results.

3. It occurs to me that the key value added of semantic technologies to decision-making is their capacity to link sources by context and meaning, which increases situational awareness and decision space. But can they probe further on their own?

Good point on the value and in a sense, yes, they can. Through extensive algorithmic operations, instructions can be embedded (and probably are for high-value situations like intelligence work), instructing the software what to do with newly discovered concepts. Instructions might then place these new discoveries into categories of relevance, importance, or associations. It would not be unreasonable to then pass documents with confounding information off to other semantic tools for further examination. Again, without human analysis along the continuum and at the end point, no certainty about the validity of the software’s decision-making can be asserted.

I can hypothesize a case in which a corpus of content contains random documents in foreign languages. From my research, I know that some of the semantic packages have semantic nets in multiple languages. If the corpus contains material in English, French, German and Arabic, these materials might be sorted and routed off to four different software applications. Each batch would be subject to further linguistic analysis, followed by indexing with some middleware applied to the returned results for normalization, and final consolidation into a unified index. Does this exist in the real world now? Probably there are variants but it would take more research to find the cases, and they may be subject to restrictions that would require the correct clearances.

Discussions with experts who have actually employed enterprise specific semantic software, underscores the need for subject expertise, and some computational linguistics training coupled with an aptitude for creative inquiry. These scientists informed me that individuals, who are highly multi-disciplinary and facile with electronic games and tools, did the best job of interacting with the software and getting excellent results. Tuning and configuration over time by the right human players is still a fundamental requirement.

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