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Is Your Brand Trusted Significantly More Than ChatGPT?

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By Chad S. White

Brands have two major levers they can pull to protect themselves from the negative effects of growing use of generative AI.

The Gist

  • AI disruption. Generative AI is set to disrupt SEO significantly.
  • Content shielding. Brands need strategies to protect their content from AI.
  • Direct relationships. Building strong direct relationships is key.

Do your customers trust your brand more than ChatGPT?

The answer to that question will determine which brands truly have credibility and authority in the years ahead and which do not.

Those who are more trustworthy than generative AI engines will:

  1. Be destinations for answer-seekers, generating strong direct traffic to their websites and robust app usage.
  2. Be able to build large first-party audiences via email, SMS, push and other channels.

Both of those will be critical for any brand wanting to insulate themselves from the search engine optimization (SEO) traffic loss that will be caused by generative AI.

The Threat to SEO

Despite racking up 100 million users just two months after launching — an all-time record — ChatGPT doesn’t appear to be having a noticeable impact on the many billions of searches that happen every day yet. However, it’s not hard to imagine it and other large language models (LLMs) taking a sizable bite out of search market share as they improve and become more reliable.

And improve they will. After all, Microsoft, Google and others are investing tens of billions of dollars into generative AI engines. Long dominating the search engine market, Google in particular is keenly aware of the enormous risk to its business, which is why it declared a Code Red and marshalled all available resources into AI development.

If you accept that generative AI will improve significantly over the next few years — and probably dramatically by the end of the decade — and therefore consumers will inevitability get more answers to their questions through zero-click engagements, which are already sizable, then it begs the question:

What should brands consider doing to maintain brand visibility and authority, as well as avoid losing value on the investments they’ve made in content?

Protective Measures From Negative Generative AI Effects

Brands have two major levers they can pull to protect themselves from the negative effects of growing use of generative AI.

1. Shielding Content From Generative AI Training

Major legal battles will be fought in the years ahead to clarify what rights copyright holders have in this new age and what still constitutes Fair Use. Content and social media platforms are likely to try to redefine the copyright landscape in their favor, amending their user agreements to give themselves more rights over the content that’s shared on their platforms.

A white robot hand holds a gavel above a sound block sitting on a wooden table.
Andrey Popov on Adobe Stock Photo

You can already see the split in how companies are deciding to proceed. For example, while Getty Images’ is suing Stable Diffusion over copyright violations in training its AI, Shutterstock is instead partnering with OpenAI, having decided that it has the right to sell its contributors’ content as training material to AI engines. Although Shutterstock says it doesn’t need to compensate its contributors, it has created a contributors fund to pay those whose works are used most by AI engines. It is also giving contributors the ability to opt out of having their content used as AI training material.

Since Google was permitted to scan and share copyrighted books without compensating authors, it’s entirely reasonable to assume that generative AI will also be allowed to use copyrighted works without agreements or compensation of copyright holders. So, content providers shouldn’t expect the law to protect them.

Given all of that, brands can protect themselves by:

  • Gating more of their web content, whether that’s behind paywalls, account logins or lead generation forms. Although there are disputes, both search and AI engines shouldn’t be crawling behind paywalls.
  • Releasing some content in password-protected PDFs. While web-hosted PDFs are crawlable, password-protected ones are not. Because consumers aren’t used to frequently encountering password-protected PDFs, some education would be necessary. Moreover, this approach would be most appropriate for your highest-value content.
  • Distributing more content via subscriber-exclusive channels, including email, push and print. Inboxes are considered privacy spaces, so crawling this content is already a no-no. While print publications like books have been scanned in the past by Google and others, smaller publications would likely be safe from scanning efforts.

In addition to those, hopefully brands will gain a noindex equivalent to tell companies not to train their large language models (LLMs) and other AI tools on the content of their webpages.

Of course, while shielding their content from external generative AI engines, brands could also deploy generative AI within their own sites as a way to help visitors and customers find the information they’re looking for. For most brands, this would be a welcome augmentation to their site search functionality.

2. Building Stronger Direct Relationships

While shielding your content is the defensive play, building your first-party audiences is the offensive play. Put another way, now that you’ve kept your valuable content out of the hands of generative AI engines, you need to get it into the hands of your target audience.

You do that by building out your subscription-based channels like email and push. On your email signup forms, highlight the exclusive nature of the content you’ll be sharing. If you’re going to be personalizing the content that you send, highlight that, too.

Brands have the opportunity to both turn their emails into personalized homepages for their subscribers, as well as to turn their subscribers’ inboxes into personalized search engines.

Email Marketing Reinvents Itself Again

Brands already have urgent reasons to build out their first-party audiences. One is the sunsetting of third-party cookies and the need for more customer data. Email marketing and loyalty programs, in particular, along with SMS, are great at collecting both zero-party data through preference centers and progressive profiling, as well as first-party data through channel engagement data.

Another is the increasingly evident dangers of building on the “rented land” of social media. For example, Facebook is slowly declining, Twitter has cut 80% of its staff to avoid bankruptcy as its value plunges, and TikTok faces growing bans around the world. Some are even claiming we’re witnessing the beginning of the end of the age of social media. I wouldn’t go that far, but brands certainly have lots of reasons to focus more on those channels they have much more control over, including the web, loyalty, SMS, and, of course, email.

So, the disruption of search engine optimization by generative AI is just providing another compelling reason to invest more into email programs, or to acquire them. It’s hard not to see this as just another case of email marketing reinventing itself and making itself more relevant to brands yet again.

Feature Image Credit: Andrey Popov on Adobe Stock Photo

By Chad S. White

Chad S. White is the author of four editions of Email Marketing Rules and Head of Research for Oracle Marketing Consulting, a global full-service digital marketing agency inside of Oracle. Connect with Chad S. White:  

Sourced from CMSWIRE

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Is Your Brand Trusted Significantly More Than ChatGPT?

No Comments
By Chad S. White

Brands have two major levers they can pull to protect themselves from the negative effects of growing use of generative AI.

The Gist

  • AI disruption. Generative AI is set to disrupt SEO significantly.
  • Content shielding. Brands need strategies to protect their content from AI.
  • Direct relationships. Building strong direct relationships is key.

Do your customers trust your brand more than ChatGPT?

The answer to that question will determine which brands truly have credibility and authority in the years ahead and which do not.

Those who are more trustworthy than generative AI engines will:

  1. Be destinations for answer-seekers, generating strong direct traffic to their websites and robust app usage.
  2. Be able to build large first-party audiences via email, SMS, push and other channels.

Both of those will be critical for any brand wanting to insulate themselves from the search engine optimization (SEO) traffic loss that will be caused by generative AI.

The Threat to SEO

Despite racking up 100 million users just two months after launching — an all-time record — ChatGPT doesn’t appear to be having a noticeable impact on the many billions of searches that happen every day yet. However, it’s not hard to imagine it and other large language models (LLMs) taking a sizable bite out of search market share as they improve and become more reliable.

And improve they will. After all, Microsoft, Google and others are investing tens of billions of dollars into generative AI engines. Long dominating the search engine market, Google in particular is keenly aware of the enormous risk to its business, which is why it declared a Code Red and marshalled all available resources into AI development.

If you accept that generative AI will improve significantly over the next few years — and probably dramatically by the end of the decade — and therefore consumers will inevitability get more answers to their questions through zero-click engagements, which are already sizable, then it begs the question:

What should brands consider doing to maintain brand visibility and authority, as well as avoid losing value on the investments they’ve made in content?

Protective Measures From Negative Generative AI Effects

Brands have two major levers they can pull to protect themselves from the negative effects of growing use of generative AI.

1. Shielding Content From Generative AI Training

Major legal battles will be fought in the years ahead to clarify what rights copyright holders have in this new age and what still constitutes Fair Use. Content and social media platforms are likely to try to redefine the copyright landscape in their favour, amending their user agreements to give themselves more rights over the content that’s shared on their platforms.

A white robot hand holds a gavel above a sound block sitting on a wooden table.
Andrey Popov on Adobe Stock Photo

You can already see the split in how companies are deciding to proceed. For example, while Getty Images’ is suing Stable Diffusion over copyright violations in training its AI, Shutterstock is instead partnering with OpenAI, having decided that it has the right to sell its contributors’ content as training material to AI engines. Although Shutterstock says it doesn’t need to compensate its contributors, it has created a contributors fund to pay those whose works are used most by AI engines. It is also giving contributors the ability to opt out of having their content used as AI training material.

Since Google was permitted to scan and share copyrighted books without compensating authors, it’s entirely reasonable to assume that generative AI will also be allowed to use copyrighted works without agreements or compensation of copyright holders. So, content providers shouldn’t expect the law to protect them.

Given all of that, brands can protect themselves by:

  • Gating more of their web content, whether that’s behind paywalls, account logins or lead generation forms. Although there are disputes, both search and AI engines shouldn’t be crawling behind paywalls.
  • Releasing some content in password-protected PDFs. While web-hosted PDFs are crawlable, password-protected ones are not. Because consumers aren’t used to frequently encountering password-protected PDFs, some education would be necessary. Moreover, this approach would be most appropriate for your highest-value content.
  • Distributing more content via subscriber-exclusive channels, including email, push and print. Inboxes are considered privacy spaces, so crawling this content is already a no-no. While print publications like books have been scanned in the past by Google and others, smaller publications would likely be safe from scanning efforts.

In addition to those, hopefully brands will gain a noindex equivalent to tell companies not to train their large language models (LLMs) and other AI tools on the content of their webpages.

Of course, while shielding their content from external generative AI engines, brands could also deploy generative AI within their own sites as a way to help visitors and customers find the information they’re looking for. For most brands, this would be a welcome augmentation to their site search functionality.

2. Building Stronger Direct Relationships

While shielding your content is the defensive play, building your first-party audiences is the offensive play. Put another way, now that you’ve kept your valuable content out of the hands of generative AI engines, you need to get it into the hands of your target audience.

You do that by building out your subscription-based channels like email and push. On your email signup forms, highlight the exclusive nature of the content you’ll be sharing. If you’re going to be personalizing the content that you send, highlight that, too.

Brands have the opportunity to both turn their emails into personalized homepages for their subscribers, as well as to turn their subscribers’ inboxes into personalized search engines.

Email Marketing Reinvents Itself Again

Brands already have urgent reasons to build out their first-party audiences. One is the sunsetting of third-party cookies and the need for more customer data. Email marketing and loyalty programs, in particular, along with SMS, are great at collecting both zero-party data through preference centers and progressive profiling, as well as first-party data through channel engagement data.

Another is the increasingly evident dangers of building on the “rented land” of social media. For example, Facebook is slowly declining, Twitter has cut 80% of its staff to avoid bankruptcy as its value plunges, and TikTok faces growing bans around the world. Some are even claiming we’re witnessing the beginning of the end of the age of social media. I wouldn’t go that far, but brands certainly have lots of reasons to focus more on those channels they have much more control over, including the web, loyalty, SMS, and, of course, email.

So, the disruption of search engine optimization by generative AI is just providing another compelling reason to invest more into email programs, or to acquire them. It’s hard not to see this as just another case of email marketing reinventing itself and making itself more relevant to brands yet again.

By Chad S. White

Chad S. White is the author of four editions of Email Marketing Rules and Head of Research for Oracle Marketing Consulting, a global full-service digital marketing agency inside of Oracle.

Sourced from CMSWIRE

chatgpt,  digital experience, search, email marketing, artificial intelligence, generative ai, artificial intelligence in marketing

 

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Is Your Brand Trusted Significantly More Than ChatGPT?

No Comments
By Chad S. White
Brands have two major levers they can pull to protect themselves from the negative effects of growing use of generative AI.

The Gist

  • AI disruption. Generative AI is set to disrupt SEO significantly.
  • Content shielding. Brands need strategies to protect their content from AI.
  • Direct relationships. Building strong direct relationships is key.

Do your customers trust your brand more than ChatGPT?

The answer to that question will determine which brands truly have credibility and authority in the years ahead and which do not.

Those who are more trustworthy than generative AI engines will:

  1. Be destinations for answer-seekers, generating strong direct traffic to their websites and robust app usage.
  2. Be able to build large first-party audiences via email, SMS, push and other channels.

Both of those will be critical for any brand wanting to insulate themselves from the search engine optimization (SEO) traffic loss that will be caused by generative AI.

The Threat to SEO

Despite racking up 100 million users just two months after launching — an all-time record — ChatGPT doesn’t appear to be having a noticeable impact on the many billions of searches that happen every day yet. However, it’s not hard to imagine it and other large language models (LLMs) taking a sizable bite out of search market share as they improve and become more reliable.

And improve they will. After all, Microsoft, Google and others are investing tens of billions of dollars into generative AI engines. Long dominating the search engine market, Google in particular is keenly aware of the enormous risk to its business, which is why it declared a Code Red and marshalled all available resources into AI development.

If you accept that generative AI will improve significantly over the next few years — and probably dramatically by the end of the decade — and therefore consumers will inevitability get more answers to their questions through zero-click engagements, which are already sizable, then it begs the question:

What should brands consider doing to maintain brand visibility and authority, as well as avoid losing value on the investments they’ve made in content?

Protective Measures From Negative Generative AI Effects

Brands have two major levers they can pull to protect themselves from the negative effects of growing use of generative AI.

1. Shielding Content From Generative AI Training

Major legal battles will be fought in the years ahead to clarify what rights copyright holders have in this new age and what still constitutes Fair Use. Content and social media platforms are likely to try to redefine the copyright landscape in their favour, amending their user agreements to give themselves more rights over the content that’s shared on their platforms.

A white robot hand holds a gavel above a sound block sitting on a wooden table.
Andrey Popov on Adobe Stock Photo

You can already see the split in how companies are deciding to proceed. For example, while Getty Images’ is suing Stable Diffusion over copyright violations in training its AI, Shutterstock is instead partnering with OpenAI, having decided that it has the right to sell its contributors’ content as training material to AI engines. Although Shutterstock says it doesn’t need to compensate its contributors, it has created a contributors fund to pay those whose works are used most by AI engines. It is also giving contributors the ability to opt out of having their content used as AI training material.

Since Google was permitted to scan and share copyrighted books without compensating authors, it’s entirely reasonable to assume that generative AI will also be allowed to use copyrighted works without agreements or compensation of copyright holders. So, content providers shouldn’t expect the law to protect them.

Given all of that, brands can protect themselves by:

  • Gating more of their web content, whether that’s behind paywalls, account logins or lead generation forms. Although there are disputes, both search and AI engines shouldn’t be crawling behind paywalls.
  • Releasing some content in password-protected PDFs. While web-hosted PDFs are crawlable, password-protected ones are not. Because consumers aren’t used to frequently encountering password-protected PDFs, some education would be necessary. Moreover, this approach would be most appropriate for your highest-value content.
  • Distributing more content via subscriber-exclusive channels, including email, push and print. Inboxes are considered privacy spaces, so crawling this content is already a no-no. While print publications like books have been scanned in the past by Google and others, smaller publications would likely be safe from scanning efforts.

In addition to those, hopefully brands will gain a noindex equivalent to tell companies not to train their large language models (LLMs) and other AI tools on the content of their webpages.

Of course, while shielding their content from external generative AI engines, brands could also deploy generative AI within their own sites as a way to help visitors and customers find the information they’re looking for. For most brands, this would be a welcome augmentation to their site search functionality.

2. Building Stronger Direct Relationships

While shielding your content is the defensive play, building your first-party audiences is the offensive play. Put another way, now that you’ve kept your valuable content out of the hands of generative AI engines, you need to get it into the hands of your target audience.

You do that by building out your subscription-based channels like email and push. On your email signup forms, highlight the exclusive nature of the content you’ll be sharing. If you’re going to be personalizing the content that you send, highlight that, too.

Brands have the opportunity to both turn their emails into personalized homepages for their subscribers, as well as to turn their subscribers’ inboxes into personalized search engines.

Email Marketing Reinvents Itself Again

Brands already have urgent reasons to build out their first-party audiences. One is the sunsetting of third-party cookies and the need for more customer data. Email marketing and loyalty programs, in particular, along with SMS, are great at collecting both zero-party data through preference centers and progressive profiling, as well as first-party data through channel engagement data.

Another is the increasingly evident dangers of building on the “rented land” of social media. For example, Facebook is slowly declining, Twitter has cut 80% of its staff to avoid bankruptcy as its value plunges, and TikTok faces growing bans around the world. Some are even claiming we’re witnessing the beginning of the end of the age of social media. I wouldn’t go that far, but brands certainly have lots of reasons to focus more on those channels they have much more control over, including the web, loyalty, SMS, and, of course, email.

So, the disruption of search engine optimization by generative AI is just providing another compelling reason to invest more into email programs, or to acquire them. It’s hard not to see this as just another case of email marketing reinventing itself and making itself more relevant to brands yet again.

Feature Image Credit: Andrey Popov on Adobe Stock Photo

By Chad S. White

Chad S. White is the author of four editions of Email Marketing Rules and Head of Research for Oracle Marketing Consulting, a global full-service digital marketing agency inside of Oracle. Connect with Chad S. White:  

Sourced from CMSWIRE

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Major brands are unwittingly supporting AI-generated junk content online with their ads

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By Luke Hurst

 has led to an increase in websites producing low-quality or fake content – and major brands’ advertising budgets may be funding them.

The Internet is awash with not only low-quality content, but content that is misleading, misinformation, or completely false.

The availability of generative artificial intelligence (AI) tools such as OpenAI’s ChatGPT and Google’s Bard, meanwhile, has meant AI-generated news and information has added to this tidal wave of content over the past year.

A new analysis from NewsGuard, a company that gives trust ratings to online news outlets, has found the proliferation of this poor quality, AI-generated content is being supported financially thanks to the advertising budgets of major global brands, including tech giants and banks.

The adverts appear to be generated programmatically, so the brands aren’t necessarily choosing to advertise on the websites that NewsGuard dubs “unreliable AI-generated news and information websites (UAINs)”.

According to NewsGuard, most of the ads are placed by Google, and they fail to protect the companies’ brand safety – as many legitimate companies don’t want to be seen to be advertising on sites that host fake news, misinformation, or just low-quality content.

NewsGuard, which says it provides “transparent tools to counter misinformation on behalf of readers, brands, and democracies,” defines UAINs as websites that operate with little or no human oversight, and publish articles that are written largely or entirely by bots.

Their analysts have added 217 sites to its UAIN site tracker, many of which appear to be entirely financed by programmatic advertising.

Incentivised to publish low-quality content

Because the websites can make money from programmatic advertising, they are incentivised to publish often. One UAIN the company identified – world-today-news.com – published around 8,600 articles in the week of June 9 to June 15 this year. That’s an average of around 1,200 articles a day.

The New York Times, by comparison, publishes around 150 articles a day, with a large staff headcount.

NewsGuard hasn’t named the big brands that are advertising on these low-quality websites, as they do not expect the brands to know their ads are ending up on those sites.

They did say the brands include six major banks and financial-services firms, four luxury department stores, three leading brands in sports apparel, three appliance manufacturers, two of the world’s biggest consumer technology companies, two global e-commerce companies, two US broadband providers, three streaming services, a Silicon Valley digital platform, and a major European supermarket chain.

Many brands and advertising agencies have “exclusion lists” that stop their ads from being shown on unwelcome websites, but according to NewsGuard, these lists aren’t always kept up to date.

In its report, the company behind the Internet trust tool says it contacted Google multiple times asking for comment about its monetisation of the UIAN sites.

Google asked for more context over email, and upon receiving the additional content as of June 25, Google has not replied again.

Google’s ad policies are supposed to prohibit sites from placing Google-served ads on pages that include “spammy automatically-generated content,” which can be AI-generated content that doesn’t produce anything original or of “sufficient value”.

A previous report from NewsGuard this year highlighted how AI chatbots were being used to publish a new wave of fake news and misinformation online.

In their latest research, conducted over May and June this year, analysts found 393 programmatic ads from 141 major brands that appeared on 55 of the 217 UAIN sites.

The analysts were browsing the sites from the US, Germany, France, and Italy.

All of the ads identified appeared on pages that had error messages generated by AI chatbots, which say things such as: “Sorry, as an AI language model, I am not able to access external links or websites on my own”.

More than 90 per cent of these ads were served by Google Ads, a platform that brings in billions in revenue for Google each year.

By Luke Hurst

Sourced from euronews.next

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3 open source NLP tools for data extraction

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Unstructured text and data are like gold for business applications and the company bottom line, but where to start? Here are three tools worth a look.

Developers and data scientists use generative AI and large language models (LLMs) to query volumes of documents and unstructured data. Open source LLMs, including Dolly 2.0, EleutherAI Pythia, Meta AI LLaMa, StabilityLM, and others, are all starting points for experimenting with artificial intelligence that accepts natural language prompts and generates summarized responses.

“Text as a source of knowledge and information is fundamental, yet there aren’t any end-to-end solutions that tame the complexity in handling text,” says Brian Platz, CEO and co-founder of Fluree. “While most organizations have wrangled structured or semi-structured data into a centralized data platform, unstructured data remains forgotten and underleveraged.”

If your organization and team aren’t experimenting with natural language processing (NLP) capabilities, you’re probably lagging behind competitors in your industry. In the 2023 Expert NLP Survey Report, 77% of organizations said they planned to increase spending on NLP, and 54% said their time-to-production was a top return-on-investment (ROI) metric for successful NLP projects.

Use cases for NLP

If you have a corpus of unstructured data and text, some of the most common business needs include

  • Entity extraction by identifying names, dates, places, and products
  • Pattern recognition to discover currency and other quantities
  • Categorization into business terms, topics, and taxonomies
  • Sentiment analysis, including positivity, negation, and sarcasm
  • Summarizing the document’s key points
  • Machine translation into other languages
  • Dependency graphs that translate text into machine-readable semi-structured representations

Sometimes, having NLP capabilities bundled into a platform or application is desirable. For example, LLMs support asking questions; AI search engines enable searches and recommendations; and chatbots support interactions. Other times, it’s optimal to use NLP tools to extract information and enrich unstructured documents and text.

Let’s look at three popular open source NLP tools that developers and data scientists are using to perform discovery on unstructured documents and develop production-ready NLP processing engines.

Natural Language Toolkit

The Natural Language Toolkit (NLTK), released in 2001, is one of the older and more popular NLP Python libraries. NLTK boasts more than 11.8 thousand stars on GitHub and lists over 100 trained models.

“I think the most important tool for NLP is by far Natural Language Toolkit, which is licensed under Apache 2.0,” says Steven Devoe, director of data and analytics at SPR. “In all data science projects, the processing and cleaning of the data to be used by algorithms is a huge proportion of the time and effort, which is particularly true with natural language processing. NLTK accelerates a lot of that work, such as stemming, lemmatization, tagging, removing stop words, and embedding word vectors across multiple written languages to make the text more easily interpreted by the algorithms.”

NLTK’s benefits stem from its endurance, with many examples for developers new to NLP, such as this beginner’s hands-on guide and this more comprehensive overview. Anyone learning NLP techniques may want to try this library first, as it provides simple ways to experiment with basic techniques such as tokenization, stemming, and chunking.

spaCy

spaCy is a newer library, with its version 1.0 released in 2016. spaCy supports over 72 languages and publishes its performance benchmarks, and it has amassed more than 25,000 stars on GitHub.

“spaCy is a free, open-source Python library providing advanced capabilities to conduct natural language processing on large volumes of text at high speed,” says Nikolay Manchev, head of data science, EMEA, at Domino Data Lab. “With spaCy, a user can build models and production applications that underpin document analysis, chatbot capabilities, and all other forms of text analysis. Today, the spaCy framework is one of Python’s most popular natural language libraries for industry use cases such as extracting keywords, entities, and knowledge from text.”

Tutorials for spaCy show similar capabilities to NLTK, including named entity recognition and part-of-speech (POS) tagging. One advantage is that spaCy returns document objects and supports word vectors, which can give developers more flexibility for performing additional post-NLP data processing and text analytics.

Spark NLP

If you already use Apache Spark and have its infrastructure configured, then Spark NLP may be one of the faster paths to begin experimenting with natural language processing. Spark NLP has several installation options, including AWS, Azure Databricks, and Docker.

“Spark NLP is a widely used open-source natural language processing library that enables businesses to extract information and answers from free-text documents with state-of-the-art accuracy,” says David Talby, CTO of John Snow Labs. “This enables everything from extracting relevant health information that only exists in clinical notes, to identifying hate speech or fake news on social media, to summarizing legal agreements and financial news.

Spark NLP’s differentiators may be its healthcare, finance, and legal domain language models. These commercial products come with pre-trained models to identify drug names and dosages in healthcare, financial entity recognition such as stock tickers, and legal knowledge graphs of company names and officers.

Talby says Spark NLP can help organizations minimize the upfront training in developing models. “The free and open source library comes with more than 11,000 pre-trained models plus the ability to reuse, train, tune, and scale them easily,” he says.

Best practices for experimenting with NLP

Earlier in my career, I had the opportunity to oversee the development of several SaaS products built using NLP capabilities. My first NLP was an SaaS platform to search newspaper classified advertisements, including searching cars, jobs, and real estate. I then led developing NLPs for extracting information from commercial construction documents, including building specifications and blueprints.

When starting NLP in a new area, I advise the following:

  • Begin with a small but representable example of the documents or text.
  • Identify the target end-user personas and how extracted information improves their workflows.
  • Specify the required information extractions and target accuracy metrics.
  • Test several approaches and use speed and accuracy metrics to benchmark.
  • Improve accuracy iteratively, especially when increasing the scale and breadth of documents.
  • Expect to deliver data stewardship tools for addressing data quality and handling exceptions.

You may find that the NLP tools used to discover and experiment with new document types will aid in defining requirements. Then, expand the review of NLP technologies to include open source and commercial options, as building and supporting production-ready NLP data pipelines can get expensive. With LLMs in the news and gaining interest, underinvesting in NLP capabilities is one way to fall behind competitors. Fortunately, you can start with one of the open source tools introduced here and build your NLP data pipeline to fit your budget and requirements.

Feature Image Credit: TippaPatt/Shutterstock

By

Isaac Sacolick is president of StarCIO and the author of the Amazon bestseller Driving Digital: The Leader’s Guide to Business Transformation through Technology and Digital Trailblazer: Essential Lessons to Jumpstart Transformation and Accelerate Your Technology Leadership. He covers agile planning, devops, data science, product management, and other digital transformation best practices. Sacolick is a recognized top social CIO and digital transformation influencer. He has published more than 900 articles at InfoWorld.com, CIO.com, his blog Social, Agile, and Transformation, and other sites.

Sourced from InfoWorld

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Four Ways Generative AI Can Be Incorporated To Accelerate Business Strategies And Operations

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By Forrester

Generative AI (gen AI) was born on November 30, 2022, with the release of ChatGPT, and it’s been moving 100 miles an hour ever since, drawing in 100 million people and counting. As new and surprisingly powerful as gen AI is, we can already see how companies will incorporate gen AI capabilities into their businesses’ strategies and operations. Our experience with two earlier, explosive technologies show you how.

  1. The BYO explosion of the late 2000s taught us how to incorporate employee-led disruption. We learned that when employees brought personal technology to solve customer and business problems. We empowered, guided, and protected employees and the firm while taking advantage of the new value that personal technologies in business brought.
  2. The mobile, social, original internet explosions taught us how to respond to and take advantage of customer-led disruption. We built mobile apps to help customers in their mobile moments of need; we adopted social media communications to improve engagement and collaboration; and we tooled up to take full advantage of the business models shaped by the internet.

Technology executives should prepare for generative AI to follow both paths and sprint into your business through four doors:

  • Bottom-up. Some of the 100 million people already using generative AI work for you. As you learned in the BYOD era, employees will adopt any tool that makes them more successful. The hyperadoption of gen AI leads to rampant BYOAI adoption. You can’t stop them, not fully. Your job is to put up guardrails that protect the firm’s IP and teach the skills of responsible AI. You need guardrails because your company IP is at risk. Just like with the original onslaught of BYO, you need to tune in now and empower, guide, and protect employees and the firm. Sharpen your listening tools and network sniffers. Revisit and promote your responsible AI policies ASAP. Your response to BYOAI will shape your top-down approach to gen AI, because employees will have elevated their robotics quotient and will be ready to go.
  • Top-down. Gen AI will unlock the value of 10-plus years of investments in data, insights, and artificial intelligence, including machine-learning models. This is where your investments in trusted AI will pay off, because you’re ready to use them. Already, the hyperscalers and software-as-a-service platform providers have announced and will trickle release gen AI-infused applications. Already, service providers and you are using TuringBots to generate and test code. Already, you’re incorporating marketing content generated from text prompts to hyperpersonalize engagement. And soon, you’ll overhaul your usability with text-based interfaces to business and analytics applications. Every part of your business will have ideas on how to use generative AI, mostly to optimize, automate, or augment something. Some will be great. Pick the ones that are easiest, safest, and most practical to deploy first.
  • Outside-in. Customers’ expectations for what gen AI can do for them are rising faster than anybody can keep up with. Every day, there is a new application using gen AI to do something useful. The latest I saw was a “free” cover-letter generator using GPT-4. (“Free” means that they’re accumulating your job preferences to resell as insights.) Microsoft triggered the search wars with OpenAI in Bing, and Google is now full-on engaged with Bard. Already, in the US, 35% of Gen Zers and 25% of Millennials have used bots to help buy hard-to-find inventory. That bot habit will be supercharged with gen AI, raising expectations even higher. Your job starts by anticipating where customers’ adoption will directly affect your company. If a customer has a better idea of your product landscape than your salespeople, that’s not good. If they are getting gen AI-powered customer care from a competitor and not you, not good. If your competitors’ stuff is in a next-generation recommendation engine and yours isn’t, that’s not good. Just like with mobile, your response will be to ramp up your customer-facing gen AI capabilities inside-out.
  • Inside-out. As you move through the gen AI opportunity thicket, you will quickly identify ways to help customers and deliver more value with your own gen AI-infused applications. Customer care or empowering frontline employees will be an early payoff, we expect. But you’ll find opportunities to streamline customer onboarding, hyper personalize engagement, provide better customer self-service, and stimulate a new round of value creation like what was triggered by mobile apps. Sort the scenarios based on the readiness of your data, the impact you will have, and your confidence that you can anticipate and manage the costs that go along with gen AI licensing and computing. The technical architectures are still in flux, but we believe that it will incorporate layers of intelligence — some of yours, some from others, and some public — protected by control gates for inputs and outputs and piped together into gen AI-infused applications. This “layers, gates, and pipes” approach will help you scale, take advantage of all the capabilities, and give you intense visibility into how it’s going and where the costs lie.

By Ted Schadler

This post was written by VP, Principal Analyst Ted Schadler and it originally appeared here. Follow me on Twitter or LinkedIn. Check out my website. 

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Welcome to the final phase of software eating the world. SaaS companies beware, you’re on the menu this time.

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By Stephanie Palazzolo

  • With its coding capabilities, generative AI is making it easier to develop software.
  • This could disrupt the way software is created, distributed, and used, VCs and startup founders say.
  • However, the death of the traditional SaaS company still seems a long way off.

While ChatGPT has been wowing the public, behind the scenes investors and technologists are beginning to talk about a deeper disruption to the inner workings of the established software industry.

A new potential framework for software, whose earlier iteration was coined  “malleable software” by researcher Philip Tchernavskij, describes a future where generative AI and humans work together to customize tooling and even create entire applications.

This outcome would flip the traditional software industry on its head, calling into question the value of SaaS companies in a world where everyday people can build software themselves.

“No-code was the first step,” said Matt Turck, a partner at venture capital firm FirstMark. “This is the final chapter of software eating the world, where a bunch of people can create enterprise software within the enterprise.”

This would represent quite a reversal for the industry. Software-as-a-Service companies have been the disruptors for a decade, not the disruptees. They have sky-high valuations because investors are betting their subscription revenue will continue steadily rising for many years to come. If generative AI really catches on, though, that future may look very different.

Democratizing tech creation

Venture capitalists and startup founders have been obsessed with the idea of democratizing tech creation for years, as seen by the rise of low-code and no-code startups like Airtable, last valued at $11 billion, and Webflow, which landed a $4 billion price tag last year.

Some technical knowledge was still required to build most software. Now, though, the emergence of generative AI tools like GitHub Copilot has opened up the ability to generate code using just natural language, Ethan Kurzweil, a partner at Bessemer Venture Partners, told Insider.

For Jake Saper, a general partner at Emergence Capital, the use cases that stand to be disrupted first are simple, low-risk tasks and applications in small and midsize businesses. These instances offer the lowest chance of business disruption and require the least cross-company coordination, he said.

Vertical software companies taking existing technologies and making them easier to use in antiquated industries could also be under threat of replacement if their value-add is more around convenience versus actual product differentiation, Fika Ventures senior associate James Shecter said.

Already, technologists have begun to use generative AI tools like Copilot to build simple apps, including a trivia game and a site for discounted Amazon items.

Some later-stage tech startups are trying to get ahead of the curve by sharing the power of creation with their customers. One example can be found in knowledge base startup Guru’s AI writing assistant, which lets customers create their own custom tones of voice using generative AI. This challenges the traditional idea of software as a rigid tool with a fixed set of available actions for users, Guru cofounder and CEO Rick Nucci told Insider.

“We’ve talked about ‘platforms’ in the SaaS world for a long time, the idea that someone can create a set of foundational building blocks that customers can configure and shape to be what they want,” he said. “This is a step change that’s actually happening.”

A new era for software

Some VCs and founders believe that generative AI could not only transform the way we create technology but also the way we interact with it through ultra-personalization.

For instance, new generative AI technology could help startups create user interfaces customized to each person’s exact preferences, Bessemer partner Talia Goldberg said. Already, ChatGPT is showing early signs of this by choosing to provide certain responses in data table format, even when users don’t specifically ask for that, she explained.

In more extreme cases, entire tools could be generated by AI on the fly to replace common actions a user takes, CRV principal Brittany Walker said.

In the long term, VCs like NEA partner Vanessa Larco and investor Elad Gil believe that autonomous AI agents, rather than humans, will be the main entities interacting with software. One potential scenario could be a world where individuals have a primary AI agent that coordinates and manages a number of “micro-agents” capable of doing everything from text messaging to scheduling dinner reservations, Larco told Insider.

These types of connections and interactions — the technical plumbing that currently makes different software programs work together — is the bread and butter business of many SaaS companies. If generative AI models can do this work automatically, what will happen to these SaaS businesses?

A ‘healthy pressure’ for traditional SaaS providers

To be sure, the death of the traditional software company still seems a long way off.

First, the choice between building software yourself or buying from a third party brings with it a substantial opportunity cost.

“I don’t necessarily want to sit on my computer for 10, 12, 15 hours developing this when I can go and find something that’s ready out-of-the-box,” CRV’s Walker said. “The barrier would need to drop very low for a critical mass of people to start creating their own bespoke software.”

Additionally, paying an outside software vendor helps people put the burden of safety, maintenance, and accountability onto a third party, Emergence Capital’s Saper said.

However, even skeptics admit that the threat of generative AI to traditional SaaS will push established software companies to prove their worth.

“It’ll probably be healthy pressure because the ‘build’ decision may be more tempting because it’ll be theoretically easier to do,” Saper said. “It’s going to put pressure on software vendors to really deliver value.”

Feature Image Credit: Bing image creator

By Stephanie Palazzolo

Contact Stephanie Palazzolo using a non-work device on encrypted messaging app Signal (+1 979-599-8091), email (spalazzolo@insider.com), or Twitter DM @steph_palazzolo.

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Generative AI for Email Personalization: A Hallucination Wrapped in Confusion

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By Chad S. White

The potential for generative AI creating highly personalized emails may be premature, as current AI technology falls short in several areas.

The Gist

  • Scaling limitations. Generative AI struggles to efficiently create highly personalized emails without significant manual intervention, limiting its practicality at scale.
  • Traditional superiority. Existing personalization methods and machine learning models are more effective, reliable and risk-averse than current generative AI approaches for email personalization.
  • Unproven potential. The performance and return on investment of generative AI personalization in emails remain uncertain, with unclear benefits and possibly diminishing novelty.

Let me be blunt: We are so far away from generative AI writing personalized emails in any meaningful way. So very far. Yet, I keep hearing people suggest that generative AI will be writing highly personalized emails to individuals in the not-too-distant future.

I don’t think this is a realistic expectation at any significant scale, with any degree of automation and with a reliable expectation of increased performance — much less a positive return on investment. Let me explain why.

Not at Scale

The best example I’ve seen of generative artificial intelligence (AI) writing a one-to-one email was a meeting request for a salesperson. First, that’s a pretty generic request — and, in the example, amounted to 10 words. Not a big time-saver. Second, the salesperson is chaperoning the AI, and if they don’t like what’s written, they either have to edit it or write a follow up prompt for changes, which further reduces the time-savings. In that example, the degree to which it was personalized was achieved through manual intervention and not automatically driven by the recipient’s past behaviours, demographics or other criteria — which is the traditional definition of personalization.

While generative AI will get much better in the years ahead, that example is a very realistic example of how generative AI would be used in a “blank page” email situation right now, given generative AI’s many faults and limitations, including its propensity to “hallucinate.” That use case appropriately minimizes the risk by (1) asking for a routine request, (2) keeping the text short and (3) having the output monitored and approved by an employee before it’s sent.

Not Automated

To date, I’m not aware of any brands using ChatGPT or any other large language model (LLM) to incorporate personalized content into an email. And there’s a good reason for that: It’s unnecessary.

Traditional methods of personalization are highly effective and are far from fully utilized — due to siloed and unreliable data. And machine learning models for product recommendations and send time optimization are even less utilized. These existing tools are tried and true and offer solid returns with much more control — which is to say, with far less risk — than generative AI.

In fact, when generative AI is ultimately used for personalization, I predict it will be fed content that’s been personalized using traditional methods. Put another way, any personalization done by generative AI will be done on top of traditionally personalized content. That approach would minimize generative AI’s opportunities to introduce inaccuracies, biases, plagiarized material and other problematic content while simultaneously focusing generative AI on the kind of personalization that only it can do.

Just what are these new forms of personalization that only generative AI enables? Here are a few:

  • Tone. Some subscribers are receptive to more aggressive pitches while others are much less so. And, of course, there’s a whole spectrum of potential tones that could be used. Generative AI could rewrite copy on the fly to better align with what subscribers respond to best.
  • Vernacular. Language varies in significant ways by geographic region, education level, religious beliefs and other factors. Especially if it’s informed by sales and support correspondence, generative AI could adapt a brand’s messages to match the recipient’s language usage.
  • Image backgrounds. Generative AI for images can enable brands to create highly personalized images based on the subscriber’s location, industry and more. For example, an outdoors retailer could place a model in any number of national parks depending on the location of the subscriber or knowledge of where they like to hike or camp.

As with all personalization, a big part of the challenge will be securing enough data to make sound decisions. But even if that hurdle is cleared, there’s the question of performance and generating a return on investment.

Unproven Performance

Oracle Marketing Consulting has identified more than 170 segmentation and personalization criteria that can be used in digital marketing campaigns. However, just because you can personalize a message using a particular data point doesn’t mean your subscribers will respond positively to it. Through experience and testing, each brand must discover which factors truly move the needle for them. The same is true for generative AI personalization.

Even if you have the data to try to personalize the vernacular of your emails, for example, it’s currently unclear if this would be a winning approach. It’s likely that at least some subscribers would find this kind of personalization creepy and manipulative.

It’s also likely that some performance boosts likely wouldn’t be sustainable as the novelty wears off. The history of first-name personalization likely provides a good example. A decade and a half ago when it was new, first-name mail merges moved the needle for a while. But it quickly became a hollow gesture and a gimmick because it generally didn’t signal that the email’s body content was any more relevant to subscribers. First-name personalization got brief bumps when brands gained the ability to personalize images with a subscriber’s name and again when they could personalize videos with their name. But subscribers still view these as technological stunts that don’t signal anything meaningful.

Generative AI personalization may have the same effect, drawing attention away from your message and to the technological stunt of personalizing an image with the skyline of the person’s home city, for example. Sure, it will be cool the first time you encounter it, but the novelty will fade quickly because it’s based on relatively easy-to-obtain geographic data that doesn’t speak to the person’s needs or wants. Plus, attention-grabbing tactics often don’t translate into better performance.

Uncertain ROI

Even if generative AI personalization can boost performance, a positive return on investment is unlikely — definitely not at current “per token” price levels. And given email volumes, it’s possible that generative AI personalization may never make financial sense except for highly targeted campaigns.

It’s worth noting that many of our clients have seen lacklustre returns when using predictive subject line writing tools like Phrasee and Persado. These tools have been around for many years and are primarily driven by machine learning models where wording recommendations are based on the historical performance of a brand’s subject lines and other copy. If models that are trained on historical performance can’t reliably generate a strong ROI, marketers should be deeply sceptical that generative AI tools with no access to performance intelligence can.

Muddied Terminology

A big contributor to this premature idea of generative AI personalization is that some people are using the terms machine learning, AI and generative AI almost interchangeably. While they’re all loosely related, they’re far from the same. They operate using different algorithms and models, have different goals, are built on different datasets and are appropriate for different use cases.

With generative AI and AI in general being so hot right now, brands need to work extra hard to be clear-eyed about what’s possible now and what may perhaps be possible at some point in the future. Just like in the late ’90s when adding “.com” to company names was all the rage, now .ai domains are super popular. Just like then, in some cases these are just aspirational or opportunistic marketing moves.

Generative AI will ultimately be huge, and brands should absolutely experiment with and get comfortable with it. However, we’re in the hype-iest part of the hype cycle, so proceed with caution and ask lots of questions.

By Chad S. White

Chad S. White is the author of four editions of Email Marketing Rules and Head of Research for Oracle Marketing Consulting, a global full-service digital marketing agency inside of Oracle.

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Google Unveils Generative AI Capabilities In Gmail, Docs Ahead Of Reported GPT-4 Release

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Google today announced several tools to allow businesses to use generative AI as a way to discover and use corporate data. It also showcased how its productivity suite, Google Workspace, will incorporate AI to help write emails in Gmail and create marketing materials in Google Docs. Other apps include Sheets, and Slides.

The PaLM API, included in the announcement, is a way to build on top of Google’s language models. The API comes with an intuitive tool called MakerSuite that lets developers prototype ideas and, over time, it will have features that prompt engineering, synthetic data generation and custom-model tuning. Select developers can access the PaLM API and MakerSuite in Private Preview.

“We’re now at a pivotal moment in our AI journey,” Thomas Kurian, CEO of Google Cloud wrote in a post. “Breakthroughs in generative AI are fundamentally changing how people interact with technology — and at Google, we’ve been responsibly developing large language models so we can safely bring them to our products.”

The latest Gartner data shows that Google held 13.7% share of the global enterprise email and authoring market in 2021, with $3.4 billion in revenue. The analyst firm also expects the email and authoring market to grow to $27.9 billion in 2023.

AI will provide a platform to start, but Johanna Voolich Wright, Vice President, Product, Google Workspace, wrote in a post that is the technology is no replacement for the ingenuity, creativity, and smarts of real people.”

A list of AI-powered features that will come to Workspace apps in the future include:

  • Draft, reply, summarize, and prioritize your Gmail
  • Brainstorm, proofread, write, and rewrite in Docs
  • Bring your creative vision to life with auto-generated images, audio, and video in Slides
  • Go from raw data to insights and analysis via auto-completion, formula generation, and contextual categorization in Sheets
  • Generate new backgrounds and capture notes in Meet
  • Enable workflows for getting things done in Chat

Google’s news comes in advance of Microsoft’s virtual Future of Work with AI event on Thursday.

Microsoft Germany CTO Andreas Braun said last week the event will likely include the release a multimodal GPT-4, which OpenAI released today, as well as a ChatGPT upgrade for Microsoft 365 applications such as Word and Outlook.

Some media sites have already reported that Microsoft GPT-4 will be “multimodal” to allow AI to translate a user’s text into images, music, and video. A call canter, for example, could use the AI program to automatically convert phone conversations between employees and customers into text, according to one report.

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