New York: People trust human-generated profiles more than artificial intelligence-generated profiles, particularly in online marketplaces, reveals a study in which researchers sought to explore whether users trust algorithmically optimised or generated representations.

The research team conducted three experiments, particularly in online marketplaces, enlisting hundreds of participants on Amazon Mechanical Turk to evaluate real, human-generated Airbnb profiles.

When researchers informed them that they were viewing either all human-generated or all AI-generated profiles, participants didn't seem to trust one more than the other. They rated the human- and AI-generated profiles about the same.

That changed when participants were informed they were viewing a mixed set of profiles. Left to decide whether the profiles they read were written by a human or an algorithm, users distrusted the ones they believed to be machine-generated.

"Participants were looking for cues that felt mechanical versus language that felt more human and emotional," said Maurice Jakesch, a doctoral student in information science at Cornell Tech in America.

"The more participants believed a profile was AI-generated, the less they tended to trust the host, even though the profiles they rated were written by the actual hosts," said a researcher.

"We're beginning to see the first instances of artificial intelligence operating as a mediator between humans, but it's a question of: 'Do people want that?"

The research team from Cornell University and Stanford University found that if everyone uses algorithmically-generated profiles, users trust them. But if only some hosts choose to delegate writing responsibilities to artificial intelligence, they are likely to be distrusted. 

As AI becomes more commonplace and powerful, foundational guidelines, ethics and practice become vital.

The study also suggests there are ways to design AI communication tools that improve trust for human users. "Design and policy guidelines and norms for using AI-mediated communication is worth exploring now", said Jakesch.

Source: ETCIO

An AI news anchor will start presenting news in Arabic and English on Abu Dhabi Media, ADM, channels, after Abu Dhabi Media Company signed an agreement with Sogou Inc., a Chinese internet industry company.

The partnership cements ADM's ongoing efforts to utilise the latest technologies and Sogou's leading AI capabilities. The agreement between ADM and Sogou was signed in cooperation with the UAE Minister of State for Artificial Intelligence. The AI News Anchor is the first Arabic-speaking in the world with the ability to interact through face expressions with the Arabic language.

The announcement was made during a special signing ceremony at ADM's headquarters in the presence of Omar Sultan Al Olama, Minister of Artificial Intelligence. Dr. Ali Bin Tamim, Director General of ADM, and Wang Yanfeng, General Manager of Voice Interaction Technology Centre of Sogou, signed the agreement.

Noura bint Mohammed Al Kaabi, Minister of Culture and Knowledge Development, and Chairwoman of the Board of Directors of ADM, explained that this step aligns with the Company's strategic plans which seek to keep up with digital advances in the media industry and adopt the latest available digital technologies, particularly in AI.

Al Kaabi said the AI news anchor will support ADM's efforts to provide guided and diverse content according to the highest international standards that meet the needs of the Company's large audience base across the Arab world.

Omar Al Olama said, "The use of artificial intelligence and technological tools in the media sector will result in a qualitative leap forward within the media landscape in the UAE and the wider region. The use of these advanced solutions will benefit the industry by enriching media content, supporting media research, and providing new opportunities for young professionals to build new skillsets suited to future media channels. The deployment of this advanced technology presents us with the opportunity to further define the future of the sector in a way that benefits all members of society."

"These new developments within the media sector are in line with the UAE Government's strategy of developing unique new models and systems built around artificial intelligence and advanced technologies that support the nation's transformation into a knowledge based society and economy," he added.

Incorporating the industry-leading algorithms and latest advances in speech synthesis, image detection and deep learning, the AI news anchor presents a lifelike resemblance of a professional human anchor, providing an increasingly seamless experience for viewers. With Sogou's technology, textual input can be transformed into corresponding lip movements, providing users with a highly customizable interactive experience. With a focus on natural language processing and machine learning, Sogou has developed industry-leading capabilities in real-time audio and video synthesis.

Together, ADM and Sogou are working to explore how technological innovation can be better integrated across media platforms, providing high quality programming for audiences worldwide.

With the integration of the AI news anchor, ADM's platform will be able to provide news broadcasts more efficiently, in a range of engaging formats, and potentially 24/7/365. Sogou's technology not only provides innovative solutions for traditional media channels but also works to facilitate more natural interactions between humans and machines.

Dr Tamim said that by launching the AI news anchor, ADM is writing a new chapter in Arab media. It also keeps pace with the Company's efforts to develop all its brands according to strategic plans that aim to realize a quantum leap in providing media content across digital, TV, print, and audio platforms.

He added, "Through this agreement, Abu Dhabi Media will develop this technology and present it to its audience, enhancing the company's presence and the quality of its content, further bolstering its leading position as one of the most prestigious media establishments in the region and the world."

Wang Yanfeng said, "We are extremely pleased to partner with Abu Dhabi Media, a pioneering multi-platform media and entertainment organization. We look forward to sharing our AI News Anchor technology with an increasingly global audience. This marks the first time that Sogou's AI News Anchor technology is being leveraged by an international media platform, and together we are thrilled to bring the AI News Anchor to Arabic-speaking viewers."

Source: Khaleej Times, a U.S./India startup that develops an AI platform to help online retailers work more efficiently and sell more, has announced a $17 million Series B round.

The investment is led by Falcon Edge Capital  with participation from Japan’s Global Brain and existing backer Sequoia Capital India. Parent company Mad Street Den was founded in 2014 and it raised $1.5 million a year later; Sequoia then bought into the business via an undisclosed deal in 2016. is described as an “AI brand” from Mad Street Den and, all combined, the two entities have now raised $27 million from investors.

In an interview with TechCrunch, CEO and co-founder Ashwini Asokan — who started Mad Street Den  with her husband Anand Chandrasekaran — explained that is a “retail vertical” of Mad Street Den that launched in 2016, and that the company may add “another vertical in a year or two.” is solely focused on working with online retailers, predominantly in the fashion space, and it does so in a number of ways. That includes expected areas such as automating product tagging and personalized recommendations (based on that tag library), as well as visual search using photos as input and tailored product discovery.

Areas in which also plays that surprised me, at least, include generating human models who wear clothing items — thus saving considerable time, money and effort on photo shoots — and an AI stylist that doesn’t take human form but does learn a user’s style and helps them outfit themselves accordingly.

Tagging and visuals may appear boring, but these are hugely important areas for retailers who have huge amounts of SKUs, items for sale, on their site. Making sure the right person finds the right item is critical to making a sale, and’s goal is to automate as much of that heavy-lifting as possible. Even tagging is essential because it needs to be done consistently if it is to work properly.

Ashwini Asokan, CEO and co-founder of

More than just working correctly, aims to help online retailers, who often run a tight ship in terms of profitability, save money and get new product online and in front of consumer eyeballs quickly.

“These are solutions that optimize the bottom line for retail companies,” said Asokan, who spent over a decade working in the U.S. before returning home to India in 2015. “We are digitizing products 10X faster than you did before… you cannot afford to lose productivity and efficiency, online retail is not somewhere you can lose money.”

“We want to be that data brain mapping digital products,” she added. is now pushing into new areas, which include advertising and development of videos and marketing content.

“The future of retail is entertainment and the experience economy is the small start of that era,” Asokan said, reflecting on the trend of social media buying through platforms like Instagram  and the rise of live-streaming e-commerce in China.

“The electricity that powers all of these complicated retail interactions is content; we need to understand content and every customer style profile and merchandise,” she added.

Some of’s public customers include Macy’s and Diesel in the U.S., Latin American e-commerce firm Mercadolibre and Indian conglomerate Tata . is headquartered in Redwood City with an office in Chennai, India. Asokan said it is planning to expand that presence with new locations in Seattle, for tech hires, and Japan and Spain to help provide closer support for customers. The company doesn’t disclose raw numbers, but it said that annual revenue grew by four hundred percent in 2018, which was its third year since incorporation.


Automation, from robotic process automation to artificial intelligence, is transforming every function of every business in every industry. In fact, according to research from PWC, AI’s impact on business will be greater than the internet. The potential applications are limitless, from individualized customer marketing, to employee screening and selection, to smarter products that collect data, to automated customer support. AI has begun to change organizational processes on a scale that the re-engineering movement of thirty years ago could only imagine. Leaders of businesses that don’t move quickly to capitalize on the power of AI will be left behind.

Despite the many indicators of a transforming marketplace, almost all legacy leaders and board members still hesitate to apply artificial intelligence to corporate strategy. Perhaps wondering whether machines are beginning to complete with high priced-strategy consultants. The answer is yes. In fact, no consulting team, no matter how big, how skilled or how expensive, gather data, analyze it, and create recommendations with the speed and scale of machines. Board members and leaders who don’t believe this can simply look to see the evolution of AI powered marketing, sales and customer support. Adopting an AI powered strategy is the natural next step. No matter the application, the process is similar. The four steps of AI powered strategy:

1. Data

Creating an AI powered strategy is all about using machines and data science to chart a better and more valuable course, as opposed to using people and spreadsheets. The key ingredient is obviously data, and in this case we’re talking about data relevant to corporate strategy. That includes traditional data like financial reports and stock performance, and also alternative data, which can take many forms. Key topics for alternative data include customer sentiment, employee satisfaction, leadership capabilities, digital readiness, and many more.

It’s important to recognize that to get the most out of an AI powered strategy initiative, you need to look beyond your industry peer group to consider at all top performers. Some of the most innovative strategies are best found among today’s unicorn startups that are applying modern business model principles such as AI powered platforms and multi-sided revenue models. Given that companies are crossing industry boundaries more frequently, an industry approach is far too narrow.

Some of this data is publicly available, some is created and owned by the firms themselves, and some can be purchased—data brokers are popping up all over the place. The key questions leaders should ask are:

  • What metrics are more important for our success?
  • What investments do we believe make a difference in our trajectory?
  • What are the unmeasured, intangible items we want to understand?

 2. Analysis

Once you have the data relevant to your strategic aims and your hypotheses about what really matters, you can start your machine learning journey. Unfortunately, machines aren’t self-starters yet. This means you need some smart humans, to teach the smart machines how to think about strategy problems. The competition for top machine learning talent is stiff, but remember that you don’t really need a PhD-level scientist for most machine learning applications. There are a plethora of off-the-shelf tools that a good developer with some relevant experience can apply to your data and problems.

The goal is to begin uncovering the relationship between the data you have, and the outcomes you wish to track. Remember that it’s essential to bring a point of view to your artificial intelligence projects. You don’t want the team to be looking under every rock in hopes of finding insight, but instead to be validating and supporting what you believe to be true. The key questions you should ask are:

  • How will I position a machine learning team organizationally?
  • What are the key beliefs we would like to validate?

3. Prediction

 Once your team has begun creating algorithms that reflect your strategy beliefs (or if proven wrong, algorithms that reflect your updated understanding), you will have a new understanding of what is really driving success. Perhaps you might uncover a relationship between employee and customer satisfaction, or between research and development and revenue growth. Whatever it is, before you can act on this insight, you will want to make sure it is not just descriptive, but also predictive. That is, you want to make sure you are doing more than just describing how things stand now—you want to be sure that your insight can actually help your organization chart its future.

A good way to do this is to examine historical data and see if it does a good job of explaining “what happened next.” For example, at AIM atters we examine how organization’s investment in business models affects their stock performance over future years. This proof point can help you push your machine learning past “interesting,” and into “useful.” The key questions to ask are:

  • What does our algorithm tell us is important for strategy?
  • Do our new insights help us predict the future?
  • Does this insight apply to other companies than our own?

4. Recommendation

Once you are convinced of the predictive power of your machine learning, you can begin to derive recommendations. Transforming products, services or processes is never going to be an easy, overnight task but it does help to have some direction. The best machine learning applications for strategy will indicate clear recommendations based on their algorithms. What changes lead to what results and in what timeframes. Often there are some quick wins—short-term priorities—that will help demonstrate value and gain buy-in for bigger AI projects. Further, unlike consultants, AI powered strategy should be able to predict the quantifiable impact of recommendations based on thousands of data points. To ensure that your autonomous AI strategy agent is doing her job, ask the following questions:

  • Are we identifying processes that can be optimized, relatively inexpensively?
  • Which projects that offer great returns but require more investment?
  • Does our AI quantify the impact of changes we could make?

Once you evaluate all the alternative moves/recommendations that are available to you, and you have weighed the cost/benefit of each, it is time to move onto execution – that’s right, getting done what the machines recommend. Think of it like a GPS – the machines can only recommend routes, but for the time being, you have to do the driving!

Adopting AI is all about people

For all those companies that aren’t Apple, Amazon, Uber or Airbnb, already AI and data powerhouses, adopting AI to power strategy is likely to be real challenge. Therefore, leaders and board members need to consider their own roles in its success. Will the leadership team commit to understanding the technology? To supporting a transformative team in the face of resistance? To funding a machine powered strategy?

Research shows that most leaders are still wary of AI, while simultaneously being afraid of its impact. Now is the time to get started and adapt to these realities. Waiting much longer might leave your company looking a lot like the yellow cab companies—too far behind to ever catch up.

Source: Forbes

There are concepts and technologies that come better as a pair, just like pen and paper, knowledge and power or nuts and bolts. Ok, better, the internet and routing, the blockchain and hashing, or digital twins, say a real time digital replica of a physical device.

The Internet of Things (IoT) and Artificial Intelligence (AI) are also one of those dance partners with perfect connection that are meaningful together. And it does make sense.

IoT is about connecting machines and making use of the data generated from those machines, which is huge. In fact, IDC research group estimates that the amount of data created annually will reach 44 zettabytes in 2020 and up to 180 zettabytes (180 + 21 zeros) by 2025. And there is no end in sight to this flood of data as there are new cIonnected devices every minute.

This data needs to be processed before travelling through the networks to produce useful actions such as traffic control, climate prediction or crime detection. This is where AI needs to play an important role, be it by the means of Machine Learning, Cognitive Computing reasoning, natural language processing, speech recognition and vision (object recognition), human–computer interaction or dialog and narrative generation.

The point is stimulating intelligent behavior in machines. Could this be done some other way? Well, experts say that traditional methods of analyzing structured data are not designed to efficiently process the vast amounts of real-time information that stream from IoT devices. So, yes, quantity is an important factor in this equation.

The next level
That is the reason why the use of AI and Machine Learning is making a splash in Industrial IoT (IIoT) markets. Deloitte underlines that the combination of AI and IoT technology is helping companies “avoid unplanned downtime, increase operating efficiency, enable new products and services, and enhance risk management.” Major vendors of IoT platforms such as Amazon, GE, IBM, Microsoft, Oracle, PTC, and Salesforce have already learned some ropes and integrated AI capabilities.

Figures prove them right. IDC has estimated that in 2019, 40 percent of digital transformation initiatives will use AI services. By 2021, this figure will go up to 75 percent. On the other hand, Gartner says that more than 80 percent of IoT projects will include an AI component by 2022, in comparison to the current 10 percent.

Ricardo Santos, CEO of Heptasense and speaker at Internet Solutions World Congress (IoTSWC), acknowledges that adding AI to IoT brings the solutions to the next level. "IoT without intelligence is just big amounts of unstructured and meaningless data. AI has brought the tools for companies to understand how to leverage the value of all that information,” he says.

“Where Heptasense is concerned, AI enables the analysis of a remarkable amount of video in real-time and helps security teams detect threats without looking randomly at the cameras,” he adds.

There is no doubt that machine learning engines are a huge leap for IoT-based businesses where the ability to analyze, predict and automatically adjust to a particular need is highly prized. We’re not just talking about predictive maintenance, which is probably the brightest showcase of AI used in IIoT.

Artificial Intelligence is now being embedded in everything from logistics to healthcare, transportation, and agriculture and is expected to go much further in a variety of sectors. Platforms are there, but some experts are already arguing that putting AI at the edge, say within the devices themselves or on local servers rather than in the cloud, is the next goldmine. People constantly interacting with their digitally-assisted realities in real time will certainly require dynamic and competitive solutions.

Ambient Computing
Lasse Rouhiainen, author of Artificial Intelligence: 101 Things You Must Know Today About Our Future,holds that AI-powered devices are becoming smaller and able to perform more functions, with greater efficiency, behind the scenes. This is what he calls “Ambient Computing.”

“It’s highly likely that by 2025-2027, so many things in our daily lives will function in an ambient environment that it will be a bit the way electricity is today: something that is always working in the background, which we never think about until it stops working,” he says.

In this scenario, companies should begin to game out the potential impact of pervasive intelligence on their business, even if there are technical constraints, cultural obstacles, organizational barriers to adoption and other philosophical questions to be overcome.

These questions will be discussed at IoTSWC to help companies and organizations create their own roadmap in exploring AI’s new paths. For a reason: The advancement of AI is unstoppable. Considering this technology as a savior is woefully naïve. Yet predicting dystopian outcomes can cause potential solutions to be missed. “The new electricity,” as Andrew Ng described Artificial Intelligence and deep learning, deserves more.

Food for thought.


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