artificial intelligence Archives

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There Is a Bright Future for AI-Driven Integration

November 17, 2023 No Comments

A look at how an integration layer completes AI applications and how integrations can be done better with the help of AI.

AI is reshaping the enterprise landscape. Already, developer productivity, digital labour, email marketing, website creation, etc., seem ripe for a major transformation. It is also well understood that general AI foundation models like GPT4 and Falcon-40B need to be fine-tuned or prompt-tuned for enterprise-specific tasks, and therefore must be fed some curated data that allows for some subset of the parameters to be “adjusted,” or output changed based on new task information given in prompts.

However, training the models is one thing. Enterprise applications today live and die on access to current enterprise data. For example, an e-commerce website might return the status of the orders of a logged-in customer. Or a chat application might process the return of a product. In neither of these cases can anything useful be done without real connectivity to ( integration with) one or more enterprise applications. First, we’ll speak to how an integration layer completes AI applications.

In addition, these integrations do not magically appear. They have to be coded, and they have to be tested and maintained. Later, we’ll speak to how integrations can be done better with the help of AI.

AI Without Integration is Incomplete

How would an AI application return useful information? AI without integration is like fish without water.

In the above figure, a natural language question, “When will my package arrive?” will need to be parsed by a foundation model, and generate a GraphQL request that then accesses an enterprise data source (and in this case, third-party systems such as FedEx), and then the response needs to be used as the input to generate the output.

The above example, while simple, shows that AI foundation models must be complemented by integration and API technologies. As readers of articles from one of the authors know, we have a particular bias for GraphQL APIs. And in this case, they are especially useful since the AI application can be trained to call one universal GraphQL API, and not have to deal with the subtleties of formats and authorizations and sideways information passing if the application were to learn multiple backends.

Integration Without AI is Incomplete

However, the complement to the above is that the opposite is also true. For each of the personas and task sets in the integration space, there are benefits in the application of AI:

Integration personas in the API management domain

Developers are the primary focus of this effort in the industry today. Prior to the rise of AI, domains like API management and application integration have already evolved toward low code/no code tooling for creating integrations, enabling citizen developers with less skill and experience to use them. AI provides the ability to further augment and empower those developers in more advanced or historically specialist scenarios.
Administrators, operations folks and site reliability engineers (SREs) of integration deployments will also benefit from the application of AI. Anomaly detection on operational metrics such as API response codes, transaction rate, queue depth and on system logs are all scenarios that machine learning models are well evolved to support – and provide the administrator a sixth sense to observe and maintain the health of a system.
Product managers and business owners often being on the less technical end of the spectrum also benefit from the low-code and generative capabilities described above, supporting them to self-serve their needs for query and analysis of data to identify business trends and new revenue streams.

In all cases there are various aspects that require close watching as AI technology matures:

First, the models have to be trustworthy. The art and science of trust in AI is being created rapidly, but of course, the rate and pace of innovation in the core AI algorithms is moving even faster. At some point in time, the trust research will have to catch up with the model research.

Related to this is determinism and repeatability. In scenarios such as generating a mapping between two data objects, it is not desirable that a different mapping be created each time you ask the same question, and yet that is the case today for many foundation models as they balance probability between multiple competing options.

Critical to the effectiveness of AI capabilities is correctness. There are many well-known examples where content generated by AI is plausible at first glance, but flawed in practice. As such, today a skilled expert is often still needed to review, debug and rectify the AI-generated artefact, but as the technology matures, we expect to see growing confidence in the validity of the output that will reduce the need for human oversight.

Next, the cost of inferencing, which is often not talked about, will become the dominant OpEx, and enterprises will have to learn to trade off the size of the model and the size of the prompt (linear and quadratic influences respectively on the cost of inferencing) with the quality of the output (is it worth going from a 8B parameter model to 100B parameter model for a 2% lift in the quality of the output)?

Sensitivity of data ownership is also a key concern for many enterprises. Foundation models work most effectively when they can be trained using the largest corpus of available examples, but if those examples contain sensitive customer information or represent a competitive advantage to the enterprise, then care must be taken in how that data will be further used by the model owner.

Summary

There is a bright future for AI-driven integration, both in the application of integration to provide access to enterprise data for use by AI tools and also for application of AI to benefit the delivery of integration scenarios.

We will be publishing a whole series of articles on the topic of the influence of AI on APIs and integration, and as some of you might know, StepZen was acquired by IBM, so we will be bringing on some additional API and integration experts, such as Matt Roberts, the CTO for IBM’s Integration portfolio.

Feature Image Credit: Shutterstock.

By Anant Jhingran and Matt Roberts

Anant is the founder and CEO of StepZen, a startup with a new approach for simplifying the way developers access the data they need to power digital experiences. With a career that spans IBM Fellow, CTO of IBM’s Information Management… Read more from Anant Jhingran

Matt Roberts is Distinguished Engineer and CTO for the IBM Integration product portfolio where he has technical responsibility for offerings that cover API management, application integration, security gateways and high-speed data transfer. He has extensive experience delivering software products and…

Sourced from THENEWSTACK

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3 ways to build A.I. skills even if you don’t work in tech: ‘Suddenly your employability options go through the roof’

November 7, 2023 No Comments

By Jennifer Liu

Artificial intelligence is the hot new skill on the job market, and even those who don’t work in tech could use it to open up a new world of job opportunities.

The U.S. is leading the way in artificial intelligence and generative AI jobs, according to data from the global job search platform Adzuna. Many roles fall squarely in tech, like software engineer, product designer, deep learning architect and data scientist.

But there are plenty of non-technical roles where having the emerging skillset can give you a leg-up, says James Neave, Adzuna’s head of data science. One fast-growing role where there’s “absolutely a shortage” of qualified applicants is tax manager. Accounting and consulting firms are looking for candidates with a mix of financial and AI skills to make their business more efficient using large language models.

It can be a lucrative move, too: The average tax manager job that’ll use AI pays $100,445 a year, according to Adzuna, and the average job using the skill in general pays $146,244.

Experts say there’s also lots of opportunity for AI to be used in customer service, writing, HR, education and health-care jobs, to name a few.

As such, Neave says it would be smart for non-technical workers to consider picking up AI skills and learning how it could apply to their work: “There are brilliant opportunities for people out there who want to get their hands on these tools and get experience,” he says. “Suddenly, your employability options go through the roof.”

Neave says generalist workers can build their AI skills, and boost their employability, in three steps:

First, get to know the most popular AI tools. “Go in and get your hands on the OpenAI website, practice a few prompts and see what comes back.”
Second, seek out online resources to understand how you might apply AI to your own line of work. Neave recommends finding YouTube videos and articles that introduce how ChatGPT, the generative AI tool released in late 2022, is used in different tasks. For example, you might research more about the best way to use ChatGPT to write a blog or create automated responses to customer emails, he says. You could also look into certification and training courses online, from the University of Michigan, Coursera and other e-learning platforms.
Finally, put your new knowledge to work in some of your routine tasks. “Once you feel confident enough using it, seek out and find any way to use it in your day-to-day work,” Neave says. It’s a good idea to check with your manager about your company’s policy on using AI in your work before doing so. And get a clear understanding of what you’re allowed to input into generative AI tools and what you’re not. For example, “there’s a general proviso that workers should not enter sensitive proprietary company data into ChatGPT to get answers, as it’s a public tool,” Neave adds.

Overall, Neave says, “if a future employer is looking at your CV, it’s going to be much more powerful if you can say you’ve gotten hands-on with ChatGPT using it for a certain purpose. That’s going to be the most compelling thing for potential employers.”

Feature Image Credit: Gorodenkoff | Istock | Getty Images

By Jennifer Liu

Sourced from CNBC make it

In News

Is Your Brand Trusted Significantly More Than ChatGPT?

October 25, 2023 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:

Be destinations for answer-seekers, generating strong direct traffic to their websites and robust app usage.
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

In News

Diller confirms plans for legal action over AI publishing

October 24, 2023 No Comments

By Miranda Nazzaro

Media titan Barry Diller confirmed Sunday he and a group of “leading publishers” plan to take legal action regarding the use of published works in training artificial intelligence (AI) systems.

Diller, the chairman and senior executive of internet and media conglomerate IAC, said he thinks generative AI is “overhyped, as all revolutions that are in the very beginning,” in an interview Sunday morning with CBS’s Margaret Brennan on “Face the Nation.”

AI systems are trained and improved using large language models, which ingest compilations of written works like books, news stories and social media posts.

Diller said he and others in the publishing industry don’t agree with how AI systems take in publishers’ content.

“It’s not that either Google or Microsoft, who are the two real leaders of this in terms of, certainly Google with having a monopoly on advertising. They, too, want to find a solution for publishers,” Diller told Brennan. “The problem is they also say that the fair use doctrine of copyright law allows them to suck up all this stuff.”

“It is, it will be, long-term catastrophic if there is not a business model that allows people professionally to produce content,” Diller continued. “That would be, I think everybody agrees is catastrophic.”

Diller claimed legislation or litigation is needed to protect the copyright of publishers.

“Of course, say we’re open to commercial agreements. But on the side of those people who are depending upon advertising, Google, for instance, they say, ‘Yes, we’ll give you a revenue share,’” Diller said. “Right now, the revenue share is zero. So, what percent of zero would you like today? I mean that’s rational, but it’s not the point. The only way you get to the point is protect fair use. In other words, protect the copyright.”

Diller would not disclose or confirm who is he planning to launch litigation with, only calling them “leading publishers.”

“It took 15 years to get back paywalls that protected publishers, I don’t think that same thing is going to happen,” Diller said.

When asked if generative AI poses a threat to Hollywood studio workers’ jobs, Diller said, “In this case, I think the one-to-three-year period, not much is going to happen. But post that, there are, of course, all these issues.”

Diller is not the first to consider legal action over AI publishing. Comedian Sarah Silverman and two other authors are currently suing Meta and OpenAI for alleged copyright infringement, claiming the platforms’ AI systems were “knowingly and secretly trained” with unauthorized copies of their books.

The Associated Press announced last week it would license its archive of news stories to ChatGPT maker OpenAI to help train the AI company’s system.

Feature Image Credit: (AP Photo/Kathy Willens)

By Miranda Nazzaro

Sourced from The Hill

In News

3 Ways Ecommerce Brands Can Use AI for Personalization

October 5, 2023 No Comments

By Shane O’Neill

How AI is revolutionizing ecommerce, from personalized ads to dynamic pricing and enhanced customer support.

The Gist

AI powerhouse. AI for personalization enhances individualized ecommerce experiences.
Tech advantage. Machine learning dynamically adapts prices, boosting consumer appeal.
Customer support. AI-enabled chatbots provide personalized, emotionally intelligent assistance.

Attention ecommerce brands: The days of blanketing consumers with vaguely relevant ads are over.

Seven out of 10 consumers now expect brands to personalize ads and product recommendations, and 76% get frustrated when this doesn’t happen, according to McKinsey research.

In response, nine out of 10 businesses, including Coca-Cola, Netflix and Sephora, are investing in the practice of using artificial intelligence (AI) for personalization to give consumers a one-to-one experience, or something close to it.

In a nutshell, personalization in ecommerce uses data to show customers products and deals tailored just for them. Instead of asking shoppers to sift through a list of products, personalization uses a customer’s purchase history and browsing behaviour with the brand to suggest the most likely item that person would buy.

To return the favour, 78% of consumers are likely to make repeat purchases from companies that personalize, according to the same McKinsey report mentioned above.

Yet personalization will only boost customer satisfaction, brand loyalty and sales if it’s executed precisely. And to do that requires culling insights from droves of customer data that humans simply cannot process and analyse manually.

And this is where artificial intelligence (AI), machine learning (ML) and natural language processing (NLP) come into play for ecommerce brands.

AI for Personalization in Ecommerce

Personalization in ecommerce is still possible without AI, but it relies on grouping customers into “personas” based on shared demographics or interests. While this is an adequate approach, today’s consumer can sniff out when they’re being marketed to as a persona rather than an individual.

AI-based personalization is much more specific, using advanced algorithms to scan volumes of customer data and deliver information to you based on your own specific behaviour.

“AI’s ability to process data in real-time and adapt on the fly to create personalized experiences is a key advantage for ecommerce brands,” said Kristin Smith, managing director and retail commerce lead at Deloitte Digital. “It also helps that AI isn’t prone to human mistakes and can work 24/7.”

With advanced personalization now expected by the majority of consumers, ecommerce brands have a variety of ways to utilize AI to deliver tailored shopping experiences. Here are three of them.

1. Product Recommendations for the Individual

One of the clearest examples of using AI for personalization are the tailored product recommendations we see in emails or when logging on to our favourite ecommerce brand’s web site.

Here, complex machine learning algorithms mine your previous purchases, cart adds, product reviews, and product interactions, and generate personalized product recommendations in real time.

This customer data becomes the basis for training an algorithm that continues to learn and improve on the accuracy of recommendations as it receives new data.

Example to Emulate: Netflix

Netflix is a recommendation trailblazer. The streaming giant’s recommendation engine, called NRE (Netflix Recommendation Engine), uses algorithms to analyse data from each member’s viewing history and generates hyper personalized movie and TV show recommendations.

2. Automated Dynamic Pricing

Constantly adjusting product prices is a necessary but time-consuming task. By incorporating machine learning into pricing, ecommerce brands can automatically adjust prices in real time based on their own manufacturing costs, competitor’s prices, market demand and seasonality.

AI-based dynamic pricing benefits consumers by:

Monitoring the competition and adjusting prices to ensure customers get a fair price.
Offering real-time personalized discounts based on a customer’s behavior. For instance, if a person continually shows interest in a product, a dynamic pricing algorithm could entice that person with a time-limited discount.

Example to Emulate: Amazon

Amazon is the king of AI-based dynamic pricing. The ecommerce giant uses machine learning to update the prices of millions of products several times every day. Its repricing algorithm factors in product demand, stock availability and customer behavior. This allows Amazon to consistently offer the most competitive prices.

3. Personalized Customer Support via AI-Powered Chatbots

Using NLP and sentiment analysis, today’s chatbots understand not just text but also the emotion behind customer support requests.

When you combine sentiment, access to customer data and speedy responses, it’s easy to see why chatbots are now a personalization tool. Today’s chatbots can greet customers by name, recommend products and discounts based on purchase and browsing data, and even help customers complete online purchases.

Example to Emulate: Sephora

Most ecommerce chatbots can handle rudimentary customer inquiries, but the more innovative chatbots also serve as shopping assistants.

Cosmetics retailer Sephora is a prime example. Sephora’s website chatbot answers questions about returns and exchanges. But it’s also a virtual assistant that asks customers questions about their skin tone and makeup preferences and then gives tailored recommendations.

The Big AI Personalization Challenge: Relevant Data

The benefits of using AI for personalization are clear, but the success of your strategy hinges on your data.

Kristin Smith of Deloitte recommends that ecommerce brands ask themselves the following questions regarding customer data:

What is the quality and source of the data your brand is trying to use?
Does the brand have permission to collect and use the data they have?
How actionable and granular is the data?

“Many organizations have customer data only at a high level,” Smith said. “But high-level, demographic data does not always translate to actionable insights for personalization.”

In addition to having the skilled staff in place to implement and maintain AI tools, the entire marketing and data team should always ensure that the data the AI algorithms are using is unbiased and specific enough to actually help the customer connect with your brand and buy from you consistently.

“There will be a rabbit hole of ideas for data points AI can collect for personalization,” said Derric Haynie, head of demand generation at Pipe17 and co-founder of Ecommerce Tech.

“Maybe you’re going to test new products based on previous purchase history. Or test personalized emails based on when customers last visited the site. There’s a lot to personalize, and the nature of personalization is recognizing each person has a different customer journey, and catering to it.”

Feature Image Credit: Blue Planet Studio

By Shane O’Neill

Shane O’Neill is an award-winning journalist and content marketer with more than 20 years of experience covering digital transformation, content marketing, social media marketing, artificial intelligence, and ecommerce. His work has been recognized nationally, earning an ASBPE Award for Blogging and a Min Editorial & Design Award for Best Online Article. Shane’s experience as both a B2B journalist at CIO.com and InformationWeek and as a content marketing director at tech startups gives him a unique insider/outsider perspective on tech innovation. Connect with Shane O’Neill: https://twitter.com/smoneill

Sourced from CMSWIRE

In News

Do ads have to be real? Marketing execs weigh in on viral tricks and fakes

September 26, 2023 No Comments

By Sam Anderson

First Maybelline’s ‘fake’ giant mascara wand, then Orange’s World Cup AI fake-out. Not-quite-real ads are here, and deepfakes aren’t far behind. Should advertisers be wary? We asked The Drum Network.

Sarah Jenkins, partner and executive vice president, The Romans New York: “As creative marketers, we’re in the business of imagination. We develop brand campaigns based on insights that strike a chord with aspects of consumers’ personalities (often a playful side): ideas that push us to think beyond what’s possible. We should never, ever limit ourselves to traditional reality, because that would stifle the curiosity that’s critical for creative evolution.

“When you’re working on a campaign that has potential negative impact, you have a responsibility to disclose when things are generated by AI. But let’s not hold back from exploring the what-ifs. Consumers of all ages are craving levity; often, that comes from the powerful escapism of make-believe. Just proceed with caution, consider negative impact, and act in a way that doesn’t pose risk to individuals or groups.”

Henry Challender, associate creative director, Bray Leino: “Realness is blurry. Neither physicist nor philosopher can tell you what reality ‘really’ is. Some people (hey Elon) even contend it’s all a big simulation. But before we get into a metaphysical pickle, let’s agree on the everyday distinction between ‘real’ (genuine, authentic, true) and ‘fake’ (false, deceitful, artificial). On those terms, it’s hard to claim marketing’s ever real. Artifice is almost always baked into the deal. Are we being ‘real’ when pricing something 99p rather than £1? When we retouch that burger?

“Even the worthiest purpose-led campaign may be tinged with an ulterior motive. AI brings new toys for tricksters, making it easier to be wilfully deceptive. As the line between the real and the fake gets blurrier, perhaps transparency will set the good actors apart from the bad. But maybe the fun is in not being quite sure.”

Jordan Dale, creative director, Amplify: “Did people think that John Lewis actually sent a man to the moon in 2015? Is Tom Holland actually living as Spiderman in New York City? Does a TikTok face filter actually change my face?

“The question is: why is this Maybelline ad being judged any differently to the thousands of ‘artificial’ entertainment creations that we consume on a daily basis through our screens? That word, ‘artificial,’ has negative connotations that we should get rid of. The best stories in the world are, by definition, ‘artificial’.

“Being nowhere near the target demo of Maybelline and struggling to avoid the ad means one thing and one thing only: it’s a certified banger. The ownership from the brand on the creative process behind it (shoutout @origiful), and turning that into its own story, means they created a richer connection with their audience. Sure, there are watch-outs to consider around AI being used for malicious intent. But let’s not let that get in the way of killer creativity and storytelling.”

Alistair Robertson, creative partner, Nucco: “Fake news! Fake news!

“Authenticity in brand experiences is key, but executionally not essential. The fake lashes and similar ideas shouldn’t cause too much soul-searching. They’re the result of technology changing and good, opportunistic, creative storytelling.

“If we’re going to worry about fakes, let’s keep an eye on what really matters. Real work (digitally crafted or not) created with a brand’s endorsement that does a real-world job versus award-entry-only executions. We’re all under pressure from external creators who want the money and notoriety of not asking for permission, or caring about the negative effect fake work can have on a brand. We’ve got to protect brands and not allow our industry to worsen its self-obsessed reputation. While the wider economy remains perilous, we need to demonstrate our worth, not our egos.

“So let’s all go be beautifully, wonderfully creative, but a little less so when it comes to the truth.”

Julio Taylor, chief executive officer, Hallam: “Creativity has always been about pushing the boundaries of what is perceivably possible. From CGI to AI, we’ve been defying the limits of what’s possible for decades. But, with AI’s power to create seemingly impossible scenarios with ease, we’re about to enter an age of unprecedented scale and speed of production that will overshadow anything we’ve seen so far.

“As the world is flooded with fast, high-quality, AI-generated content, consumers will learn to tune it out, and seek authenticity and meaningful experiences in a world of neon satisfaction. Just as vinyl made a comeback during the streaming wars, human nature will drive people toward emotional fulfilment, authenticity and meaning.

Stella Thewes, account director, Disrupt: “I like the Maybelline ads because they’re obviously not real, allowing the brand to venture into new creative realms without misleading customers. However, there’s a grey area between real advertising and visibly fake ads. For example, mascara ads featuring retouched models with fake eyelashes, giving the consumer an illusion of what is possible to achieve with their product. The same goes for deep fakes. Marketers should question whether their work is ethical – either by being truthful with their claims and concepts, or by creating something that couldn’t be mistaken for reality, but still gets consumers talking.”

Joe Murgatroyd, partner and creative director, BrandNation: “Creatively, the Maybelline ad is brilliant. The magic lies in it looking real, blurring the lines between virtual and reality – and putting a new spin on DOOH.

“While the ad itself isn’t harmful, it does pose valid ethical questions on the approach amid a broader societal debate on AI and computer-generated imaging.

“Advertisers should be setting a precedent and using this technology responsibly, signposting where AI has been used. There needs to be accountability and industry policing, given that this technology can be harnessed for more unscrupulous ends (like cybercrime and disinformation). This doesn’t need to stifle creativity, but with new transformational technology, there’s a moral obligation to act with transparency.”

Diana Tran Chavez, senior vice president & group creative director, Evoke Mind+Matter: “We don’t control how technology develops around us, but as marketers we should be trying to find ways to use it to bring more creative, fantastical ideas to life – ideas that previously were discarded as ‘too difficult’ or ‘too expensive’.

“AI is transforming creativity and advertising, and in healthcare marketing and medical comms, the stakes are higher (and much more sensitive to misinformation). Without industry-wide regulation, the moral line is surely about intention. If it’s to misinform, that’s bad. But if it’s to inspire and delight, why not? If we create responsibly, and maintain our human judgment, intuition, and intelligence, we can still get the point across in an impactful and memorable way.”

Melissa Harvey, content marketer, Social Chain: “First Jacquemus, now Maybelline: brands are catching on to the appeal of synthetic out-of-home.

“As the digital world bleeds into our reality, brands’ playful embrace of digital art highlights the potential of the ‘fake out-of-home’ ad for brands, and the future of the PR stunt. Virtual activations like these more sustainable and low-cost than their real-world counterparts, and there’s no end to what you can create.

“Even if people are hoodwinked into thinking these ads are real, it doesn’t do any harm. Not directly. But conversations about the danger of deepfakes are worth having. As we increasingly rely on social media as a news source, independent research and fact-checking becomes crucial.”

Annie Shortland, digital PR executive, Builtvisible: “Deepfakes are becoming increasingly sophisticated, becoming sources of misinformation and potentially breaching consumer trust for those buying into the phenomenon. But the Maybelline advert is far from a deepfake. This inspired activation takes creativity to new heights, using AI to get more traction with a lower budget.

“Now, marketers can jump hoops to get their campaigns live and viral (mostly), without having to get buy-in from collaborators and external sources. The logistics of creating the ad ‘for real’ are not feasible, so digital activation encourages creative, inspired ideas at pace, without the roadblocks that usually come, allowing brands to reap measurable results much faster.

“Engaging in digital stunts is perfectly acceptable, provided the idea or concept isn’t harmful in its illusion. These stunts serve as a fun way for brands to flex personality and connect with audiences.”

Joe Veal, account manager, Rawnet: “As modern generations have become sceptical and unsympathetic toward overt selling, advertisers must step outside traditional boundaries to engage audiences effectively. This led to the emergence of these ‘fake’ ads using AR technology like Sony, LG, and Maybelline’s billboards in Times Square or Deutsche Telekom’s deepfake/AI work featuring manipulated images to demonstrate online misinformation.

“While this new landscape offers endless creative possibilities, an ethical question arises: just because we can, should we? Audiences are often aware of the fakeness, making it crucial for marketers to consider overall impact and avoid spreading misinformation. Failure to do so could result in harsh repercussions.”

By Sam Anderson

Sourced from The Drum

In News

Is Your Brand Trusted Significantly More Than ChatGPT?

September 15, 2023 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:

Be destinations for answer-seekers, generating strong direct traffic to their websites and robust app usage.
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.

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

In News

3 open source NLP tools for data extraction

August 9, 2023 No Comments

By Isaac Sacolick

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

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

In News

Getting the most out of ChatGPT: These are the most useful prompts to make your life easier

August 4, 2023 No Comments

By Imane El Atillah

Tailoring prompts for ChatGPT means increasingly the effectiveness of the chatbot’s responses. Here are the best tried and tested prompts to bookmark.

ChatGPT has taken the world by storm since its release, with millions of users flocking to utilise its services at an unprecedented rate.

However, while some users have found the artificial intelligence (AI) chatbot to be a useful tool, others have been less than impressed, citing issues and limitations with their interactions with it.

One key factor to consider is the way in which users communicate with it. Simple commands may not always suffice, with users needing to employ more nuanced prompts to achieve their desired outcomes.

To help users make the most of ChatGPT’s capabilities, experts on social media platforms such as Twitter have been sharing valuable insights and strategies for effective communication with the chatbot.

Why is getting prompts right so important?

ChatGPT has been facing criticism for its inability to perform specific tasks accurately and its tendencies to lie and hallucinate. However, the secret to mastering ChatGPT and getting desired outcomes is choosing the correct prompts for it.

By using specific prompts, users can navigate the chatbot more effectively and achieve more personalised responses, unlocking the full potential of ChatGPT.

The importance of tailoring perfect prompts is so valuable that companies are recruiting experts who can communicate with chatbots effectively and a new job, AI prompt engineering, has emerged in the market with a salary range of up to $300 000 (€275 346).

Euronews Next has compiled a list of the five most useful prompts and put them to the test.

Prompt 1: Simplifying complex notions

Prompt: Hey ChatGPT. I want to learn about (insert specific topic). Explain (insert specific topic) in simple terms. Explain to me like I’m 11 years old.

ChatGPT’s ability to provide clarity, use simple language and provide explanations are top tier. When asked to explain blockchain in a way an 11-year-old understands, its oversimplification of complex notions helps users to understand things outside of their expertise and with no prior knowledge of technical terms required.

Prompt 2: Generate the perfect marketing plan

Prompt: I want you to act as an advertiser. You will create a campaign to promote a product or service of your choice. You will choose a target audience, develop key messages and slogans, select the media channels for promotion, and decide on any additional activities needed to reach your goals. My first suggestion request is, “I need help creating an advertising campaign for (insert description of service or product)”

ChatGPT has access to the Internet’s database. It knows what people like, what appeals to them the most, what advertisements work well for companies and the marketing strategies to build a successful brand in any domain.

With ChatGPT on hand, the time when the success of marketing strategies is left in doubt or is a question of mere luck appears to be coming to an end.

So much so that individuals are using ChatGPT to build a whole company from scratch. Perhaps the interesting part of this development is that it is working, and by following simple step-by-step guides from the chatbot, users have been able to launch businesses and generate profit.

Prompt 3: Take advantage of expert consulting

Prompt: I will provide you with an argument or opinion of mine. I want you to criticise it as if you were <person>

Person: (insert expert name)

Argument: (insert desired topic)

No one is better at providing money-making advice than the richest man in the world. Thanks to successful people’s presence online like billionaire Elon Musk, ChatGPT is able to easily mimic their thinking process and personify them to provide relevant and helpful advice to users.

Prompt 4: Job interview simulations

Prompt: Simulate a job interview for (insert specific role). Context: I am looking for this job and you are the interviewer. You will ask me appropriate questions as if we were in an interview. I will respond. Only ask the following question once I have responded.

Provide the chatbot with enough context about the job you’re interviewing for and let it do its magic. This is a great way to practice your interview responses and get an overall idea of what questions you might get asked.

As you provide the chatbot with more and more information when responding, it will tailor its questions more effectively.

Prompt 5: Make ChatGPT write like you

Prompt: [Insert Text]

Write about (insert text topic) as the above author would write.

One of the many complaints people have about chatGPT is its inability to provide content tailored to each user. This leaves many complaining about the dullness of the responses and how in some cases it can easily be guessed that an AI wrote the piece.

However, when using the correct prompt, ChatGPT is capable of mimicking one’s own writing style and providing personalised responses.

By Imane El Atillah

Sourced from euronews.next

In News

20+ Cool AI Tools Just Added To Our List

July 19, 2023 No Comments

By Asif

Rask AI

Discover the power of AI with Rask.ai, your one-stop platform for full self-serve video localization. Harnessing the potential of artificial intelligence, Rask.ai streamlines the process of video localization, offering a time-efficient and cost-effective solution for content creators and businesses alike.

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Introducing Wisecut: The genius behind captivating video creation. Wisecut, an innovative online tool, employs the brilliance of artificial intelligence and voice recognition to revolutionize how you edit videos. Unleash the limitless potential of AI and effortlessly craft mesmerizing videos with an incredible speed!

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Maximize your product’s potential with Nuclia’s AI-powered search and generative answers. Safeguard your data privacy while unlocking limitless insights. Embrace Nuclia for AI-driven excellence!

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Introducing SoReal.AI, your gateway to mesmerizing AI-generated images. Unleash your imagination and witness the magic unfold before your eyes. Simply input your desires, and within moments, immerse yourself in a world of personalized, awe-inspiring visuals.

tinyEinstein

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Slayer AI

Slayer AI revolutionizes podcasting by crafting tailor-made shows in mere seconds.

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Introducing Fig: the cutting-edge, futuristic command line. It holds the ultimate knowledge base for your team’s hidden treasures, powerful scripts, and mystical SSH codes.

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Bid farewell to your unflattering profile image with ProfilePicture.AI, the ultimate AI-powered creator and generator of stunning profile pictures.

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Magic Studio

Step into the extraordinary realm of the Magic Studio, a creation brought to life by your own hands. Discover the mesmerizing wand that has the power to transform your pictures into pure magic.

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In today’s digital age, we’re inundated with a constant influx of emails that can prove overwhelming and time-consuming. Enter SaneBox, your trusted partner in email management, designed to simplify your life.

Pictorial.ai

Pictorial.ai: Create graphics effortlessly for your blog, social media, website, and ads. Get inspired and convey your message with reliable, ready-to-use visual masterpieces. No hassle, just captivating visuals.

Helicone

Helicone, a cutting-edge solution for harnessing the power of Generative AI, has become the go-to tool for numerous organizations looking to enhance the effectiveness of their Large-Language Model endeavours.

AI-Surge

AI-Surge ignites productivity with its cutting-edge solution, offering a dependable way to conquer the hurdles of data productivity. Experience the power of a user-friendly, versatile platform that effortlessly tackles data preparation and exploratory analysis in just minutes.

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Sourced from aitoolsclub.com