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By Rick Prelinger

Non-fungible tokens and artificial intelligence make tracing the origins of a digital object more fragile. What are the world’s archivists to do?

As an archivist, I’m excited about what disruptive innovations like non-fungible tokens (NFTs) and artificial intelligence may mean for archives. But I’m also worried. These developments pose existential threats to our field, and by extension, to the survival of human history and culture.

I give old films away for free. It started in 1999 when I was seduced by the promise, excitement, and just-felt-rightness of the gift economy. Not 30 seconds after we first met, Internet Archive founder Brewster Kahle asked me, “Want to put your film archives online for free?” Braving the new world of video digitizing and sputtery streaming changed my life. Our archival footage enabled thousands, maybe millions of artists, video makers, educators, and even post-Communist Polish village kids to remix history and bring the past into the present. I never knew how many people were using our material or who they were—but wasn’t that the point?

In 1999 the future of our archives was to be consumed, to enrich public memory with new evidence without hassle. I wanted our archives to be as ubiquitous as infrastructure, to work their way into every corner of the net, to propagate everywhere without need for attribution or credit. I wanted our archives to vanish in the web.

I still do.

But now the survival of archives as we know them is uncertain. Whether we know it or not, we all rely on a patchwork of chronically underfunded public and private institutions that hold the world’s histories and cultural heritages in trust for all of us and make them accessible. Every time we see an old photo, hear a historical recording, see a news clip, or find a family history document, it likely originated in an archive. While we see and touch massive digital archives online, most archives are still largely undigitized collections of physical media like film, video, music, photographs, and paper documents. By design, archives are deliberate and thoughtful, with a timeline designed to preserve culture “forever.” They’re not built to nimbly weather disruption.

It was only a matter of time before the market figured out a way to manufacture and sell digital scarcity, and the marketplace for cultural objects has moved well past the archival ecosystem. Artists, gamers, entertainers, athletes, and executives now sell NFTs, tokenized digital objects whose authenticity is said to be assured by the reverse traceability of blockchain transactions. The combination of Covid-19 isolation and cryptocurrency profits created a powerful incentive for digital-positive collectors to compete for these NFTs, and some creators are raking in Ethereum.

Law professor Tonya M. Evans optimistically suggests that crypto art offers Black artists and communities opportunities to bypass white art gatekeepers and “capture and own the value of the culture that they produce.” While the current boom may well go the way of the 1920s Florida land-rush hype, NFTs are the first step in what’s likely to be a robust market for unique or scarce digital objects. Many of these digital objects won’t be born-digital; instead they’ll be one-off digitized copies of physical materials, for which there could be a huge market. Who wouldn’t want to own the master digital copy of their favourite author’s journal, a photograph of Abraham Lincoln or Frederick Douglass, or the recently rediscovered newsreel of the 1919 Black Sox scandal?

Nothing could be a greater cultural and ethical shock to archives than NFTs. Prevailing archival ethics generally dictate that all users are treated equally, and that archival materials aren’t exposed or sold only to high bidders. And once archives select materials for retention, they consider themselves in most cases ethically bound to do so permanently.

If an archive has a merch business, it’s tiny: keychains and postcards. As poor a fit with archival DNA as tokenizing archive collections as NFTs may be, the possibility of leveraging digital scarcity by selling NFTs while retaining physical materials is a hefty temptation. The archival world is a world of inadequate budgets and financial constraint, filled with underpaid workers and massive, poorly resourced projects like digital preservation, and the challenging task of digitizing analog materials. Will archives be tempted by the potential upside of NFTs and tokenize digital representations of their crown jewels (or the rights to these assets)? This would worsen an already bad situation, where institutions like our Library of Congress hold physical copies of millions of films, TV programs, and recordings that can’t be touched because someone else holds the copyright. Ideally, archives and museums should own and control both the physical and digital states of its collections. That won’t happen if they have to sell or license NFTs in order to survive. And there’s another risk: Minting NFTs requires a lot of energy (though we may hope for a cleaner process), and the future security of archives is threatened by climate change. Researchers have discovered that almost all archives will be affected by risk factors like sea level rise, increased temperature, or heavy rainfall.

For those working with the raw materials of history, integrity and authenticity are the chief necessities. How do NFTs address these? While the blockchain is supposed to draw an unbroken link between creator/tokenizer and purchaser, it’s just a record of transactions that might be tainted or even bogus. We know the original Mona Lisa resides in the Louvre, but it’s very hard to identify who really created and who owns many of the millions of creative works made in the analog era. It’s nearly impossible to track who owns the even greater number of digital works made every year. So while the blockchain can help us follow sales and transfers of digital objects, how do we know the original representations made about these objects can be trusted? Already there are many NFTs on offer that are nothing more than tokenized versions of works belonging to third parties, often scraped from museum websites. This isn’t a new problem—after we put digitized films from our archives online for free, many other stock footage companies downloaded them and sold them as their own. Who will be the arbiter of which copies are closest to the original? The quagmire recalls Philip K. Dick’s The Man in the High Castle, where American craftspeople build near-perfect counterfeits of American cultural collectibles. Authenticity is based on aura, aura on belief. If many Alices sell tokenized, pirated copies of the Zapruder film to many Bobs, this bombs the blockchain with many transactions that obfuscate the fact that these might be pirated versions. The solution, obviously, is to know your source—to authenticate objects and their provenance by authenticating their owners. But can that scale in a marketplace where transactions might number in the billions?

Registries are emerging to authenticate sources and provenance, and perhaps even indemnify purchasers against false representations by sellers. These have long existed in the collectibles business. Rare coins are frequently processed by trusted grading and authentication services, which charge to inspect coins and then encapsulate them in sealed plastic slabs. While I can see this happening for the four existing colour versions of Edvard Munch’s The Scream, how would it work for millions of new digital works? Will these registries be proprietary or open, and how much will their services cost? And authentication systems ultimately rest on the accuracy of information they receive. Who would arbitrate conflicting claims between potentially millions of squatting bots feuding over provenance and authenticity issues? And could such registries be flooded with blockchain-authenticated look-alikes and deepfakes? If you think this is improbable, just look at YouTube, where Content ID, its suite of pattern-matching algorithms that’s claimed 800 million videos since its launch, supposedly enforces the rights of copyright owners by flagging unauthorized uploads. The system generates huge numbers of false positives and won’t authorize legitimate fair uses of content. And copyright to millions of videos (including many public domain works) is claimed by squatters. Fuzziness and a high error rate may be OK for a commercial service like YouTube. But it’s a disaster for cultural and historical institutions that derive income from reuse. How would a blockchain full of false transactions complicate ownership and authenticity?

One working solution is for cultural and historical institutions like archives to run their own trusted registries of digital objects. But this is expensive, and it creates further incentives for archives to monetize their holdings and become less accessible to non-commercial users, like genealogists, the group that uses archives more than anyone else. If the need to administer NFTs makes NFTs inevitable, that’s a loss. Today’s archivists are faced with the need to come to terms with NFTs without having the resources to ensure their needs are met.

NFTs make archival authenticity, and thus history, more fragile. And right now authenticity is threatened by AI.

If you’ve seen Denis Shirayev’s upscaled historical videos, you’ve seen the past enhanced by a touch of the future. He takes videos scanned from very old films, like our poignant A Trip Down Market Street Before the Fire, shot just days before the 1906 quake and fire that devastated San Francisco, upscales them to 4K, smoothes out jitter and adds colour. (Today any video editor can make something almost as good using off-the-shelf tools like Topaz Video Enhance AI.) Shirayev’s videos are beautiful and compelling, but they show you something that never was. They’re not archival; they’re fiction. Artist and author Gwen C. Katz recently demonstrated the flaws of AI colorization, showing that software substitutes a drab colour palette for the brilliant colours of historical reality, and pointing out that only primary historical evidence unaffected by our preconceptions can present a plausible image of the past. Will prettified AI-enhanced digital objects made to draw in the eyeballs of distracted web surfers push the original, less attractive evidence out of view? Will people modify archival materials in such a way as to marginalize their original source? Will established archives sit by as others embellish their collections into prettier, more marketable objects? And, worst of all, will pretty images stand in for the inconvenient, hard-to-view authentic record?

The question of “real” vs. “pretty” recently exploded when a Vice article (now removed) revealed Matt Loughrey‘s colorization and alteration of mug shots taken in a Khmer Rouge torture center just before their subjects were murdered. Cambodian genocide survivors led a protest against Loughrey’s project, which added smiles to a few victims’ faces, and the Tuol Sleng Genocide Museum and the Government of Cambodia called for his apology and for the photographs to be taken down. From the viewpoint of historical and archival authenticity, Loughrey’s call for infusing archival photos with a sense of contemporary realism was perhaps most disturbing. “The image that we’ve come to accept as standard is becoming obsolete owing to the advance in display technology,” he told the Daily Mail. “When we consider a museum or a library or a documentary, as these displays advance, which they are rapidly, the producers of these are going to be less inclined to display and use these images. They’re going to have to find new images by repurposing them.” The actual archival record, in other words, can no longer stand by itself.

Together these two developments pose an existential threat to archives. Archives won’t go under, but it’s going to take serious thinking, significant new funding, and public education to help these under-resourced, deliberate organizations respond to rapid change. The integrity and survival of these important institutions are in everyone’s interest. Archives aren’t perfect, but they can help keep us honest. They’re a force for historical accuracy and accountability, if we trust the records in their collections and know their provenance. All of us should hope the promise of NFTs and AI won’t slide us into a world of “fake archives” and speculation in archival collectibles.

I still want our film archives to be consumed freely by makers in familiar media and in media we haven’t yet begun to imagine. I still want our archives to vanish in the web. But I don’t want history and the institutions that painstakingly preserve it to disappear into an eternal and amnesic present.

Featured Image Credit: Sam Whitney; Getty Images

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Rick Prelinger is an archivist, filmmaker, writer, and educator whose collection of 60,000 films was acquired by the Library of Congress in 2002. He is currently chair and professor of film and digital media at the University of California, Santa Cruz.

Sourced from WIRED

 

Reporting by Stephen Nellis in San Francisco; Editing by Marguerita Choy

(Reuters) – Adobe Inc ADBE.O said on Monday that it has put a new set of artificial intelligence tools into its digital marketing software with the aim of helping companies sharpen their marketing campaigns.

Once known for applications like Photoshop, Adobe has become one of the biggest providers of software for running such campaigns, which businesses use to decide which of thousands of images and pieces of written to content to show to potential customers. Growth in its marketing software division has helped send shares up nearly 50% this year.

The artificial intelligence features released on Monday aid that effort by, for example, scanning and labelling thousand of product images by colour and shape, or using natural-language processing technology to read an article to determine its subject.

That makes it easier for marketing campaigns to make a recommendation, whether that means showing a person browsing an e-commerce site a pair of shoes similar to ones they have previously viewed or a news website suggesting a story on a similar subject to the one just read.

Such artificial intelligence technology has existed for several years, but using it generally required corporate marketing departments to export data from their systems and work with another division of the business to use, slowing the work down, Ali Bohra, director of strategy and product marketing for intelligence services at Adobe, said in an interview. Adobe has placed the technologies directly inside the marketing systems, reducing the need to export data.

“When you’re thinking about the need to be agile and work in real time, this is not a process that works very well,” Bohra said.

Feature Image Credit: An Adobe Systems Inc software box is seen in Los Angeles, California, U.S., March 13, 2017. REUTERS/Lucy Nicholson

Reporting by Stephen Nellis in San Francisco; Editing by Marguerita Choy

Sourced from Reuters

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China announced in 2017 its ambition to become the world leader in artificial intelligence (AI) by 2030. While the US still leads in absolute terms, China appears to be making more rapid progress than either the US or the EU, and central and local government spending on AI in China is estimated to be in the tens of billions of dollars.

The move has led – at least in the West – to warnings of a global AI arms race and concerns about the growing reach of China’s authoritarian surveillance state. But treating China as a “villain” in this way is both overly simplistic and potentially costly. While there are undoubtedly aspects of the Chinese government’s approach to AI that are highly concerning and rightly should be condemned, it’s important that this does not cloud all analysis of China’s AI innovation.

The world needs to engage seriously with China’s AI development and take a closer look at what’s really going on. The story is complex and it’s important to highlight where China is making promising advances in useful AI applications and to challenge common misconceptions, as well as to caution against problematic uses.

Nesta has explored the broad spectrum of AI activity in China – the good, the bad and the unexpected.

The good

China’s approach to AI development and implementation is fast-paced and pragmatic, oriented towards finding applications which can help solve real-world problems. Rapid progress is being made in the field of healthcare, for example, as China grapples with providing easy access to affordable and high-quality services for its ageing population.

Applications include “AI doctor” chatbots, which help to connect communities in remote areas with experienced consultants via telemedicine; machine learning to speed up pharmaceutical research; and the use of deep learning for medical image processing, which can help with the early detection of cancer and other diseases.

Since the outbreak of COVID-19, medical AI applications have surged as Chinese researchers and tech companies have rushed to try and combat the virus by speeding up screening, diagnosis and new drug development. AI tools used in Wuhan, China, to tackle COVID-19 – by helping accelerate CT scan diagnosis – are now being used in Italy and have been also offered to the NHS in the UK.

The bad

But there are also elements of China’s use of AI which are seriously concerning. Positive advances in practical AI applications which are benefiting citizens and society don’t detract from the fact that China’s authoritarian government is also using AI and citizens’ data in ways that violate privacy and civil liberties.

Most disturbingly, reports and leaked documents have revealed the government’s use of facial recognition technologies to enable the surveillance and detention of Muslim ethnic minorities in China’s Xinjiang province.

The emergence of opaque social governance systems which lack accountability mechanisms are also a cause for concern.

In Shanghai’s “smart court” system, for example, AI-generated assessments are used to help with sentencing decisions. But it is difficult for defendants to assess the tool’s potential biases, the quality of the data and the soundness of the algorithm, making it hard for them to challenge the decisions made.

China’s experience reminds us of the need for transparency and accountability when it comes to AI in public services. Systems must be designed and implemented in ways that are inclusive and protect citizens’ digital rights.

The unexpected

Commentators have often interpreted the State Council’s 2017 Artificial Intelligence Development Plan as an indication that China’s AI mobilisation is a top-down, centrally planned strategy.

But a closer look at the dynamics of China’s AI development reveals the importance of local government in implementing innovation policy. Municipal and provincial governments across China are establishing cross-sector partnerships with research institutions and tech companies to create local AI innovation ecosystems and drive rapid research and development.

Beyond the thriving major cities of Beijing, Shanghai and Shenzhen, efforts to develop successful innovation hubs are also underway in other regions. A promising example is the city of Hangzhou, in Zhejiang Province, which has established an “AI Town”, clustering together the tech company Alibaba, Zhejiang University and local businesses to work collaboratively on AI development. China’s local ecosystem approach could offer interesting insights to policymakers in the UK aiming to boost research and innovation outside the capital and tackle longstanding regional economic imbalances.

China’s accelerating AI innovation deserves the world’s full attention, but it is unhelpful to reduce all the many developments into a simplistic narrative about China as a threat or a villain. Observers outside China need to engage seriously with the debate and make more of an effort to understand – and learn from – the nuances of what’s really happening.

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Sourced from The Conversation

Sourced from Rank me Locally

As technology is advancing with a rampant pace, so are the trends of digital marketing that has changed the outlook of the industry. In today’s digital space, it is not merely enough to have a website and social media; you need more to stay ahead of the game.

The digital world is revolutionizing with brilliant ideas, trends and technologies that are rising and prevailing in 2020 and beyond. These trends are changing the way of marketing for businesses and individuals.

Here are the six innovative digital marketing trends that will own 2020:

The Rise of Video Marketing

Videos will have a rise in 2020 and beyond. It is the best strategy for businesses to market their brands online and influence customers with their brand story. The customers are keen on watching short and long length interactive live streaming videos on YouTube. Not only YouTube, but Facebook, Instagram, snap chat, and Pinterest are showing trends of live video marketing for brands.

These videos deliver enormous entertainment and information to the audience to increase traffic views by hitting like share and subscribe to social media. Video marketing will be among the top of the list in digital marketing in the year 2020 and captive huge audience attention.

A successful video marketing involves many components like attention-grabbing introduction, problem-solving content, high-quality screen resolution, compelling punch line, and call to action elements.

Interactive Content is the Future of Digital Marketing

Interactive content is the future of digital marketing. The term “Interactive Content” defines the strong interaction between the brand and the audience. It triggers viewers to take some action and engage with your website or social media to enhance a dynamic experience with users.

Today’s audiences are bored with blogs, articles, and white paper content. They need something exciting and engaging to hold them back with a website. To get over with boredom, marketers should find new ways to present content to communicate and connect with the audience. There can be plenty of ways to interact with the audience, such as infographics, live videos, contests, wizards, quizzes, e-books, and snackable content.

The snackable content is a combination of visually appealing content to grab instant attention of readers at first glance. It combines short-form and long-form content to connect with readers and enhance traffic, ranking, and lead conversion of businesses.

Emerging Augmented Reality and Virtual Reality Technology Trends in Digital Marketing

The emergence of augmented reality and virtual reality in digital marketing has taken marketing automation far beyond the imagination. Now, businesses can use augmented reality AR to bring innovation in product marketing and show products to customers from three-dimensional angles.

Virtual reality (VR) is popular among all ages of audiences in the gaming industry. These technologies are ideal in promoting 5G internet broadband with faster live streaming than before.

Many leading shopping and furniture product brands are increasing their sales and revenues by using augmented reality trends in digital marketing. There are a variety of other industry applications implementing AR and VR technologies in brand marketing in 2020.

Using Artificial Intelligence in Digital Marketing

The artificial intelligence is one of the latest trends in digital marketing that is taking the industry by storm. Businesses are using artificial intelligence in various activities like rapid and automated e-commerce transactions, product recommendation, email personalization, and content creation.

Artificial Intelligence is being used in predictive data analysis of companies and businesses to improve their sales and revenues. The use of robotics intelligence is ideal for ease of execution of tasks and improves productivity, efficiency, and profitability of businesses in the long run.

AI is a scientific technology of designing intelligent robotic machines that think and respond like humans. It allows businesses to make smarter decisions in designing websites, improving user experience, creating successful advertising campaigns, SEO A/B Testing, personalizing content, and predicting consumer behaviors.

Introducing the Era of Voice Searches and Chat bots in Digital Marketing

Voice searches are long-tailed conversational queries that anyone can ask from a device and receive a fast and instant response. Voice search has changed the way of search engine optimization and digital marketing.

The value of written type-based search queries is less effective than voice search. There are a few popular voice search devices such as Google Home, Microsoft Cortana, Google Assistant, Amazon Echo, Alexa, iPhone Siri, and android phones. These devices are ideal for translating voice-based queries and showing desired results for businesses and individuals.

Chatbots are generally used as a popup in websites to allow users to ask any query regarding a website. The sales and support chat agents are always there to help and guide customers in any technical issue. Chatbots offer written style conversation between an agent and a customer and use natural language processing technology to execute and translate query. Now with the latest advancement and innovation, there are voice chatbots available for customers to respond to them in a voice-based conversation style.

Shoppable Social Media Posts

Social media has a considerable influence on businesses to share posts content and reach their desired target audiences. The emergence of e-commerce has created an enormous demand for social media for businesses. Many companies use different social media platforms for marketing their e-commerce products to the online audience.

Shoppable social media posts are introduced by a popular social media platform known as Instagram. It allows users to directly tag and purchase shopping products from the featured post content. Businesses can also create their brand stories by signing up a new business profile account and selling their labeled products online.

Conclusion

Hence, in a nutshell, those mentioned above are the latest and innovative digital marketing trends that will own in 2020. Some essential trends in digital marketing are last long to stay, such as organic search engine optimization, search engine marketing, and social media marketing.

Online reputation management, email marketing, content marketing, lead generation, and inbound marketing also have tremendous value and demand in digital marketing. These trends are ideal for increasing ranking, traffic, lead conversion, sales, and revenue for businesses.

Rank Me Locally is the best Local SEO Company that offers affordable Local SEO Services to clients. We are among the leading Local SEO Agencies in the United States that have a team of dedicated and qualified Local SEO Consultants to bring your website to the top of Google.

Sourced from Rank me Locally

Sourced from BOSS Magazine

Software as a service (SaaS) has enjoyed significant growth as a market over the past decade, with more and more businesses and consumers making use of cloud-powered solutions to run…

Software as a service (SaaS) has enjoyed significant growth as a market over the past decade, with more and more businesses and consumers making use of cloud-powered solutions to run apps and services.

Today, new pressures are shaping the SaaS ecosystem, with artificial intelligence (AI) arguably being the most impactful and disruptive of the bunch.

Here is a look at how AI is already unleashing the true potential of SaaS and what features it is making available to the mainstream today which would have been impossible to offer just a year or two earlier.

Customization

There are growing expectations surrounding the provision of bespoke experiences that are tailored to the needs and habits of individual users, and with the help of AI in software development, it is simpler for SaaS to meet this demand.

AI has been especially impactful with regards to enhancing the natural language processing abilities of SaaS software, allowing apps to adapt to user behaviours and habits rapidly, and without the need for extensive reworking at the back end by developers. Machine learning algorithms allow apps to get better at serving the requirements of users over time, making on-the-fly customization and personalization a breeze to offer.

Support

Another labour-intensive aspect of running software in the cloud is the need to provide support for users so that you can troubleshoot problems and answer any questions that they might have. AI is being used to automate the support process, with the widespread adoption and ever-improving sophistication of chatbots being the best example of this.

Rather than needing a whole team of human workers on hand around the clock to field questions, AI-fuelled chatbot solutions can provide in-app support with no additional overheads at any time of the day or night.

This does not mean that human customer service agents can be eliminated entirely; rather AI helps to augment existing support resources and give businesses more flexibility when handling common complications. There is even growing support for chatbot amongst consumers, showing that SaaS is helping to normalize this aspect of digital services.

Analytics

SaaS solutions can be used to generate a huge number of data points relating to user behaviors and actions, but the process of analyzing these vast volumes of information manually to draw actionable conclusions is almost impossible. AI can, therefore, be invaluable as a means of providing excellent analytical capabilities that not only let you digest data after the fact but ultimately use it to predict how things will play out in the future.

In turn, this allows for flaws in the software to be ironed out before they lead to user dissatisfaction and disengagement while ensuring that the valuable data which flows from each app does not go to waste.

Security

Security has always been a concern in the digital sphere and is even more pertinent now that the majority of apps and services are being migrated to the cloud rather than installed or hosted locally.

As with data analytics, the challenge of facing up to the ever-evolving security threats that face SaaS solutions is almost insurmountable for human experts alone. However, AI offers up the answer in the form of security platforms that are not only ever-vigilant to existing risks but also capable of learning from and adapting to new threats as and when they emerge.

The IT security industry is large and still growing, but AI promises to make defensive services not only more effective but also more affordable, as well as ensuring that they are as widely available as possible.

Sourced from BOSS Magazine

By Pradeep Aradhya

Today’s customers interact with products or brands at various stages, from brand awareness to comparison to purchase to product support and loyalty. Most marketers are focused on awareness and maybe a little on experience during comparison and loyalty. They rarely get deeper, either because they do not have access to the full customer experience cycle, because there has not been a way to handle all of it, or because they are not incentivized to do so,

In recent years, marketing has started to delve into customer support to standardize customer experience. However, machine learning (ML) and artificial intelligence (AI) have started to provide tools that can offer much deeper insight and actions. So, what are the true and far-reaching opportunities for marketers with AI?

Imagine being able to serve every customer or potential customer at any touch point in the cycle as if there were a live salesperson there who instantly knew that person’s history with the brand — the summary of clickstream, purchase history, loyalty, support encounters and sentiment. Imagine that salesperson’s efficiency if they could instantly have at their disposal a best offer or reward, or information to nudge the customer to engage or repeat engage with the brand.

So far, we have seen data analytics and maybe some ML used to analyze clickstreams and some digital visitor behaviour to make recommendations for cross-selling and up-selling. Much more recently, we have seen chatbots used to scale customer support, as well as do voice-based sentiment analysis with tools based in some form of ML, AI, natural language processing and speech recognition. While these have been steps in the right direction, they are very narrow. They are point solutions based on isolated data pools. They take no steps toward either determining broader context or finding the best opportunities for marketers and sales folks to increase acquisition and retention. Catering to customer segment is old-fashioned. With AI, we can cater to individuals based on extended or immediate history with the brand.

The current capabilities of ML and AI allow not only the complete context of the customer to be gathered instantly, but also to serve the experience most likely to drive customers further down the funnel. The following key elements are required to exploit this phenomenal opportunity that is now available to marketing and sales.

  1. To enable capture of complete customer context: Data from the following different pools needs to be joined and integrated into a single customer 360 view or hub: store and online purchase data, website and mobile clickstream, social behavior, support encounters, sentiment analysis, demographics and loyalty data. Joining data across anonymous and authenticated customer sessions is still not easy, but there are workarounds. Data so integrated provides a complete immediate and extended history of any customer.
  2. To create the right experience for each context: Whether created manually or generated via automated methods, content and experiences that cater to customer intent and context at various points in the customer life cycle have to be developed. Everything from appropriately keyworded variations of product or service descriptions and videos to granular reward schemes for loyalty or for support scenarios to different purchase experiences that surprise and delight must be generated and kept at the ready.
  3. To serve the best experience each time: Previously, data analysis and even ML was used to determine hidden customer segmentation and to predict product or service demand. Marketing and sales then manually arranged experiences to be served, after some A/B and even multivariate testing. Now AI can be used to run acquisition and retention maximization models. AI can run multivariate testing using the data and the experiences developed in the previous steps. Based on training gained from such testing (at scale or in smaller focus areas), AI can instantly determine the most likely experience that would cause new customer acquisition or repeat sale and retention.

All of the above should only be done in stages. Companies have spent years attempting to pull together customer 360 databases. Start by integrating online clickstreams with demographics and purchase and support data. Bring in-store purchase, sentiment analysis and social data at a later stage. With the same objective of leveraging online first, create content and experiences in digital formats before taking on in-store experiences or field sales agent empowerment. Retention models to achieve or increase customer lifetime value are much more lucrative and less infested with stakeholders. Honing and running retention models before new customer acquisition models is a great way to prove out AI capabilities with the fewest unnecessary concerns and the least amount of interference.

Relinquishing manual or even rules-based recommendation is not an easy cultural shift, but already, results with ML and AI outperform anything marketing and sales have otherwise done.

  • Best Western used AI-based tips and tricks for various destinations to help travelers plan and realized a 48% increase in traffic to Best Western Locations.
  • The Humane Society used ML messaging and realized an 86% lift in shelter pet adoption by reaching friends and cohabitants in the relationship graphs of those who had been identified visiting a shelter and showing any intentions to adopt.

Marketers reacting in retrospect to segmentation analysis is old-school and far too late. The new tech-savvy marketer wants to wield individual customer context and experiences nimbly, in real time, to catch the most new customers and achieve the greatest customer lifetime value. The race is on. Brands that win this race will set themselves so far ahead of the rest that they will not be caught for years.

Feature Image Credit: Getty

By Pradeep Aradhya

CEO of Novus Laurus. Business and transformation strategist. Digital technology, film, and food investor. Read Pradeep Aradhya’s full executive profile here.

Sourced from Forbes

By Randhir Kumar

With the rise of AI, the Indian learning and e-learning landscape have seen considerable change. Here’s how the power of AI is being capitalised in the field of education.

Artificial intelligence (AI) is one of the major technological innovations in recent times, set to revolutionise industries across verticals. In simple terms, AI is the capacity of a computer/machine to collect, anticipate, analyse information, recognise patterns and, consequently, perform actions as opposed to natural human intelligence.

AI has permeated industries throughout the years, aiding and executing taxing responsibilities such as customer service (voice assistant services), in automobiles, robotics, etc. Its presence has likewise penetrated the education sector, which can be further corroborated by the surge in edtech startups in India.

With the rise of AI, the Indian learning and e-learning landscape have seen considerable change. Here’s how the power of AI is being capitalised in the field of education:

Modernisation of education

AI aids the process of channeling focus on core concepts of all subjects, while embedding interdisciplinary concepts on the same platform. AI has allowed education to be personalised for students to fit their unique needs. It aids the process of designing and developing technology, which facilitates quick resolution of queries with independent support to students as opposed to a teacher teaching a class of 20+ students.

Meeting unique needs

AI also brings value on the table by procuring information on the unique requirements on students, courses, and their syllabi to develop a holistic and robust learning trajectory. Leading and competing players in the e-learning market have harnessed AI’s ability to recognise patterns, and congregate data by mapping online movement/footprints from numerous sources to decipher the finest techniques of learning for students in general and individuals in particular. It has filled the gaps, which a normal classroom lacks, through clever coding, algorithms, and big data, enabling students to choose subjects, concepts that suit them best in accordance with where their interests lie. In this manner, content isn’t imposed on students unlike in schools.

Ultimately, the student excels in their academic goals and paves way for a brighter future. The same ability throws statistics on how quickly or slowly an individual can digest information besides preferences, interests, and degree of involvement to further help e-learning platforms tailor their offerings for them. AI has ensured a personalised learning journey while making education more relevant to students, parents, and teachers.

Feedback that supports growth

AI lends support by monitoring and mapping the learning graph of a student or an individual. Once the data has been acquired on the individual, it’s scrutinised using advanced analytics for insight generation (strengths and weaknesses) to develop tailored plans for an individual to ensure that the person gets the most out of his/her course online.

Learning can be fun

Artificial intelligence can turn learning into an enjoyable experience. One of the newest ways teachers can engage students online or in a classroom is by using simulation. Leading gaming companies have been using simulation to engage their customers for decades. Many medical and flying institutes have started using computer-powered simulations to put candidates/students in a real-life situation wherein students can react and interact, based on which one can be judged.

Breaking geographical barriers

Students can move beyond the geographies of their classroom/room without actually stepping out. AI makes communication with peers on a global scale easier through the Internet. With the help of third-party apps, getting insights on a subject matter of concern to the student is just a click away.

This ability can be further used to enable partnerships on a global scale, similar to an electronic student exchange program, where students or individuals could come together and provide answers to solutions or create a collective craft.

Beyond learning

We have adopted AI in all forms, from chatbots to adtech in our lives, making our life simple and easier. We have embraced a greater scientific approach, which enhances strategy, targeting, insight, creativity, knowledge, and experience. Basic and redundant processes are made easy, like the creation of profiles, which require consolidating information of students, which also, at times requires constant updating. Another understated potential is regular updates to the online alumni community. To that end, a feed framework can be created on a subscription basis, to update students with developments in the field and enable them to take calculated steps to enhance their career curve.

Segmentation framework

Another facet of how AI is used in the edtech industry is in the segmentation framework model. Under this, AI aids the study of different data sources that are available to understand their structures for profiling. The data, which is profiled, addresses different objectives, for instance:

Assessment: Whenever an individual poses a question, with the help of advanced analytics, AI can determine the level of understanding of the student/individual. It helps in assessing whether the analytical intelligence of the student/individual is on par with the level of the concept and whether they have a proper understanding of the subject.

Cross-selling of content: AI identifies the learning patterns of a student or an individual basis, which it provides them with supplementary learning content to ensure that the individual grasps the concept in its entirety.

Dropout: When a student or an individual chooses to opt out of a course or arbitrarily drops out, AI is able to determine the reason by determining how interested the individual was in the subject.

With the baseline data and touch points, educators can then proceed to develop predictive model layers over the data to draw and retrieve inferences, which will form the foundation of all future engagements.

By Randhir Kumar

Sourced from YOURSTORY

By Ephrat Livni

It’s easy enough to forge a signature for fraudulent purposes. However, until recently, some things—like our voices—have been distinctive and difficult to mimic. Not so in our brave new world.

A new kind of cybercrime that uses artificial intelligence and voice technology is one of the unfortunate developments of postmodernity. You can’t trust what you see, as deep fake videos have shown, or what you hear, it seems. A $243,000 voice fraud case, reported by the Wall Street Journal, proves it.

In March, fraudsters used AI-based software to impersonate a chief executive from the German parent company of an unnamed UK-based energy firm, tricking his underling, the energy CEO, into making an allegedly urgent large monetary transfer by calling him on the phone. The CEO made the requested transfer to a Hungarian supplier and was contacted again with assurances that the transfer was being reimbursed immediately. That too seemed believable.

However, when the reimbursement funds had yet to appear in accounts and a third call came from Austria, with the caller again alleging to be the parent company’s chief executive requesting another urgent transfer, the CEO became suspicious. Despite recognizing what seemed to be his boss’s voice, the CEO declined to make the transfer, realizing something was amiss.

Although the CEO recognized the familiar accent and intonations of the chief executive, it turns out that the boss wasn’t making the call. The funds he transferred to Hungary were subsequently moved to Mexico and other locations and authorities have yet to pinpoint any suspects.

Rüdiger Kirsch, a fraud expert at insurer Euler Hermes, which covered the victim company’s claim, tells the Journal that the insurance company has never previously dealt with claims stemming from losses due to AI-related crimes. He says the police investigation into the affair is over and indicates that hackers used commercial voice-generating software to carry out the attack, noting that he tested one such product and found the reproduced version of his voice sounded real to him.

Certainly, law enforcement authorities and AI experts are aware of voice technology’s burgeoning capabilities, and the high likelihood that AI is poised to be the new frontier for fraud. Last year, Pindrop, a company that creates security software and protocols for call centers, reported a 350% rise in voice fraud between 2013 and 2017, primarily to credit unions, banks, insurers, brokerages, and card issuers.

By pretending to be someone else on the phone, a voice fraudster can access private information that wouldn’t otherwise be available and can be used for nefarious purposes. The ability to feign another’s identity with voice is easier than ever with new audio tools and increased reliance on call centers that offer services (as opposed to going to the bank and talking to a teller face-to-face, say). As the tools to create fakes improve, the chances of criminals using AI-based voice tech to mimic our voices and use them against us are heightened.

By Ephrat Livni

Sourced from QUARTZ

Are retailers hearing the call of mobile?

A recent report by Forrester found that smartphones were used in more than one-third of U.S. retail sales in 2018, from product research to checkout. For retailers looking to convert greater mobile sales, they might want to reevaluate their social media advertising.

According to a Think With Google survey, 51% of smartphone users purchased an item from a different company than originally intended, due to messaging appearing exactly when they needed it. That suggests social media advertising campaigns could attract new customers, if deployed strategically. Designed to help retailers capitalize on this opportunity, marketing platform SmarterHQ launched an Ad Personalization program on Tuesday morning.

In order for brands to acquire and retain valuable customers, they must have a personalized, cross-channel strategy that spans ad platforms,” said Michael Osborne, president & CEO at SmarterHQ. “But until now, targeting within these platforms hasn’t been comprehensive enough. Syncing first-party data to power highly relevant ads often requires extra manual work and IT resources, which has hindered these efforts.”

The program builds on SmarterHQ’s existing behavioral marketing offering, which centers on collecting omnichannel data to inform brand messaging. Through Ad Personalization, the same omnichannel analysis can be integrated with the user’s Facebook and Google advertising to create individualized and customized campaigns. These can then work in conjunction with email, web and mobile pushes that the user already coordinates through SmarterHQ.

But SmarterHQ isn’t the only company taking advantage of the growing emphasis on social advertising and the new data technology available. At Pattern89, an artificial intelligence (AI) platform for digital marketers, data from all of its customers is anonymized and run through the company’s algorithms. This turns more than 100 billion impressions into 2,900 dimensions of analysis that are available to all users.

Pattern89 Computer screen
The Pattern89 platform is popular with e-commerce brands looking to roll out new campaigns every few days. This made possible by AI, which is able to process data and generate new recommendations daily.
CREDIT: Pattern89

“One footwear retailer wouldn’t see the results of another footwear retailer because the machine doesn’t look at the data in that way,” said RJ Talyor, CEO and founder of Pattern89. “Instead, it looks at all of the red shoes, or all of the ads that are targeting women between the ages of 17 and 23. It anonymizes all this data, runs the analysis and identifies where the biggest opportunities are for you.”

Users of the program are then presented with a daily to-do list to optimize advertising performance, which Talyor estimates can be completed in five minutes. A new feature introduced this week, Gemini, enables users to automate the daily to-do list by clicking a “do it for me” button. Then there is the Creative Planner program, which makes broader advertising strategy recommendations based on AI learnings.

Artificial intelligence is becoming more common in retail; Salesforce projects that the percentage of retail and consumer goods marketers that are leveraging some form of AI will increase to 70%, from 20%, in the next two years. It also found that, during the 2018 holiday season, AI-powered recommendations yielded 14% higher, on average, order value.

Nevertheless, many retailers are still resistant to AI findings. As Pattern89’s algorithm looks at data from across industries, users receive insights collected from unexpected places; the same customer might buy both a pair of shoes and a mattress, revealing trends that work across contexts. But these recommendations can seem counterintuitive or untrustworthy, such as when one woman’s brand was told it should target men in its advertising. The brand chose not to follow the suggestion, but Talyor believes that not trusting AI is a mistake.

“There’s no bias in the machine; it’s looking for the lowest opportunity,” said Talyor. “It requires humans to intervene — and sometimes humans are unwilling to part with their intuition and their experience. But others are and when they do, they find untapped pockets of opportunity.”

Want more?

How Chatting on Social Media Could Bring Big Business Gains

This Mobile Platform Wants to Help Everyone Become an Influencer

This Acquisition Means More Data and Actionable Insights for Retailers

Feature Image: Salesforce found that 87 percent of consumers begin their shopping journey with digital tools, such as smartphones.CREDIT: Glenn Hunt/EPA-EFE/Shutterstock

 

Sourced from FN

Sourced from Droid Men

61% of marketers declare that Artificial Intelligence (AI) is the most critical element of their data strategy.

Marketing is a fast-paced discipline. If you want to succeed in it, you must stay at the bleeding edge of new breakthroughs.

By 2020, 85% of customer relations will be automated. You, therefore, need to position your brand to stay competitive.

AI is probably the biggest technology push of our time revolutionizing every aspect of marketing. To get the most out of automated customer service, marketers are turning to AI advertising for strategies that can deliver higher value.

Here are some ways in which you can incorporate AI into your marketing to keep up with the times.

1. Chatbots

A Chatbot is an AI software that is capable of simulating a conversation (chat) with a user in natural language.

Brands have taken to using chatbots to interact with their clients on messaging apps like WhatsApp, Slack, and Facebook Messenger.

Through these bots, brands can answer queries customers frequently ask in a speedy fashion.

Since they retain a customer’s data after the interaction, they can build on that information to deliver more personalized experience during the next interaction.

That reinforced learning pattern only makes the experience better for the customer.

2. User Experience (UX)

When you have a website, the user experience will significantly influence whether the customer will return to it or not.

You can use AI to collect information on customers and understand their likes, intent, and desire. Data points to gather here include location, the devices they use to visit the website, demographics among others.

As the user keeps browsing the site, you get to gain more insights about them and deliver appropriate offers and content that resonates with their needs.

AI marketing that helps shape your user experience for the better has the potential to increase your conversion rate.

3. Search Engines

People today take it for granted that they can search for anything on Google and find a relevant result.

Such a scenario is the result of decades of research and analysis on how to create and deliver a more intuitive search experience for customers.

After Google deployed RankBrain, its machine-learning based algorithm, many businesses saw the value of such an application.

Nowadays consumer companies like Amazon take advantage of artificial intelligence marketing tactics that can help them deliver relevant results to you.

Innovations like natural language processing and semantic search determine the relationships between products.

When you run a search, they help recommend similar items and auto-correct mistakes so that you can find the right products.

4. Predictive Analysis

Predictive analysis is the use of data, machine learning techniques, and statistical algorithms to draw conclusions on future actions based on the data.

Using predictive analysis you can determine the probability of a prospect becoming a client.

Thus, depending on the conclusion your draw you can determine how much resources you will dedicate to converting the prospect.

Another area predictive analysis is useful in is pricing. Using this tool, you can more accurately determine which price point will deliver more sales for you.

That information can then contribute towards your value proposition marketing.

5. Email Marketing

Email marketing is a crucial part of any brand’s marketing mix as it is one of the few digital assets they fully control.

But with the rise in sources of data from 10 in 2017 to 15 in 2019, marketers may struggle to personalize these emails.

AI can help you unify the piles of information on a subscriber and learn how to reach more effectively.

For example, it can help you determine how many times to send the email per user and what time of the day is best to send it.

6. Digital Marketing

Pay-per-click (PPC) advertising is a cornerstone for any digital campaign. Typically, PPC ad campaigns are usually managed by an in-house team or a large agency.

AI can help you discover new channels your competition may be unaware of.

Machine learning techniques can help you optimize the layout, bids, targeting, and copy for your campaign.

You will be able to realize a higher return on advertising per campaign by using AI in marketing online.

7. Social Listening

Every brand needs to have a presence on social media to extend its customer service to where its customers are.

Consequently, it is essential that brands have their finger on the pulse of what users are saying about them.

Natural language processing innovation has made it possible for brands to hear what users and the public at large like or dislike about them.

Therefore, they can get ahead of any potential issues before they blow up.

You can also use AI in social listening to identify potential purchasers and nudge them towards a sale.

8. Audience Targeting

Customers today have come to expect a certain level of personalization, and as a marketer, you can’t fail on this expectation.

To help you create more accurately personalized campaigns, you will need to segment your customers as finely as possible.

AI can draw on the data you have on your customers and identify a common variable that can help shape your communication with a specific audience.

For example, if your data shows you that a significant number of your customers are into destiny power leveling, you can set up banner ads to effectively reach them.

9. Voice-Based Services

In the past few years, voice-based services have gained quite some traction.

Voice assistants such as Alexa, Siri, and Cortana have made it infinitely easier for consumers to search and place an order by speaking at their devices.

Natural language processing technology helps improve speech recognition so that customers can successfully issue commands.

AI can help you deploy voice-based services to provide your customers with an avenue for easier sales and interaction.

Beef up Your AI Advertising Strategies or Get Left Behind

Artificial Intelligence is making a big impact on marketing. High automation levels in what once used to be human job roles call for precise AI advertising strategies by brands.

Are you concerned with the impact technology will have on your business? Thumb through our content to learn more about how you can use innovative breakthroughs to power your business forward.

Sourced from Droid Men