Companies know that a high IQ can help drive business value. But the analyst outfit Forrester Research believes that if companies are going to successfully work side by side with artificially intelligent systems, they’re also going to need a high “RQ.”
RQ, or robotics quotient, is a measurement of how competent a company will be at automation and AI implementation. The Forrester assessment is based on three main areas: people, leadership and organizational structures. A fourth area, trust, will influence the three main categories and change depending on the type of technology being deployed.
J.P. Gownder, a Forrester analyst serving CIOs, described RQ as the “human contribution” companies need when deploying automation and AI technologies. “It’s not just about the bots; it’s not just about artificial intelligences,” he said in a July presentation at the New Tech and Innovation 2018 conference in Boston. “It’s about real people, real leaders and real organizational structures that you need to put in place to make sure you’re most likely to succeed.”
Automation and AI technologies are on a spectrum from more deterministic, where A always leads to B, to more probabilistic, where A could lead to B but could also lead to C or to D.
And these probabilistic systems create a new wrinkle for companies: No matter how swanky the user interface or how cutting-edge the technology, probabilistic systems can produce incorrect — and even illogical — results that can erode the trust humans have in the machine’s abilities.
Gownder pointed to IBM Watson as an example. During its Jeopardy! debut in 2011, Watson answered a final question about U.S. cities with “Toronto,” causing the audience to gasp. When researchers did a post-mortem, it became clear that even Watson doubted the response. Using probabilistic judgement, the machine determined that Toronto had only a 30% chance of being correct, but it was the best answer it could come up with at the time.
These “Toronto moments,” as Forrester now refers to them, “teach us something about the intersection between human beings and AI and the trust that is part of this,” Gownder said.
The more probabilistic a system is, the more human intervention it might need. But designing systems and processes that strike a balance between trust and intervention will be a challenging step for companies. That’s where Forrester believes RQ will come in handy.
What is RQ?
The robotics quotient is a self-assessment that “measures the ability of individuals and organizations to learn and adapt to and collaborate with automated entities,” Gownder said. It’s composed of 39 characteristics that Forrester regards as a collection of automation and AI best practices.
The higher the score, the more prepared a company is to tackle the new challenges that come with automation and AI technologies. But RQ doesn’t just measure readiness, according to Gownder. It also enables CIOs to “identify gaps or areas where you need to prioritize resources before you make a big bet on automation and AI,” he said.
The 39 characteristics fall into one of three categories — people, leadership and organizational structure. People, for example, are measured across different dimensions — such as facilitation, which considers how effective an employee might be at communicating with an automated entity, and perception, which includes things like basic digital literacy and “constructive ambition,” or an eagerness to learn.
For leaders, the RQ highlights vision, adaptability, the ability to inspire trust and influence. The final category refers to IT employees and beyond; CIOs will need to influence the C-suite and even the board of directors to secure the budget, buy-in and support that automation and AI tools can demand. “The CIO is no longer a benign dictator who has all the power,” Gownder said. “This is the creation of an ecosystem across business units with lots of participation from the workers themselves.”
Organizational structures will also need to adapt. Automation and AI may require new titles such as bot manager, new training and mentoring opportunities for humans and machines alike, new processes that encourage human-machine team creation, and new metrics. “After all, we can have all the good intentions, and the well-educated employees and the leaders who are on board,” Gownder said, “but if we do not create structures, processes and budgets — the b word — we’re going to have a hard time getting this through.”
Don’t forget about trust
The categories of people, leadership and the organization are then measured against one final category — trust. Gownder called trust “a multiplier in this model.” Automation and AI technologies exist on a spectrum from transparent to opaque, and where the technology falls on that spectrum will influence employee trust.
“If you’re implementing something that is very transparent, that is very deterministic, your employees will bring a high level of inherent trust to the machine. They’re used to these sorts of systems,” Gownder said. “If you’re using probabilistic systems, where the machine is often uncertain of its results, then you’re going to have a higher burden of RQ investment.”
Forrester’s model breaks down the complexity of trust by providing a numeric value for how deterministic the technology is, how transparent the technology is and how much change the technology could have on the workplace.
The changes that automation and AI will have on the workplace could be a sensitive area for leaders, especially as automation and AI instigate changes in the workforce. “As you might imagine, when employees are losing their jobs as part of a deployment of automation, you magnify the mistrust among remaining employees,” Gownder said. “It raises the bar for the change management.”
But the efforts could be worthwhile. As repetitive tasks become automated, job satisfaction generally goes up, Gownder said. And although AI remains in its early stages, it is poised to transform how companies operate and interact with customers.
Whether companies choose Forrester’s RQ method or not, Gownder argued that an organizational competency in AI and automation is needed.
“If you want to be successful in creating a mixed workforce that incorporates digital workers, human workers, lots of automated processes, lots of probabilities, lots of real-time data and AI, you’re going to have to measure your people, your leaders, your organization and the inherent trust that is associated with technology,” he said.
Benefits of Using AI for Facebook Retargeting In 2021
Artificial intelligence has really transformed the state of digital marketing. A growing number of marketers are using AI to connect with customers across various platforms. This includes Facebook.
There are a lot of great reasons to integrate AI technology into your Facebook marketing campaigns. One of the benefits is that you can use retargeting. AI algorithms have made it easier to reach customers that have already engaged with your website. These users might be a lot more likely to convert, which will help you grow your sales and improve your brand image.
AI Can Help Improve Your Facebook Marketing Dramatically with Retargeting
Untapped resources that come from engagement can lead to a better understanding of Facebook retargeting. SAAS SEO agency shared some recommendations that are solid, and a great starting point for any business. To understand Facebook retargeting with AI technology in-depth, take these tips to heart when organizing your resources.
What is Facebook Retargeting?
On average, each Facebook user clicks on ads at least eight times per month. These are considered to be high intent clicks from the biggest advertising platform in the world. Even the most successful marketing and advertising campaigns miss consumers on their first run.
Retargeting uses Facebook campaigns most essential tools to target specific people based on their most relevant data. This is one of the best ways to use data to improve your social media marketing strategies.
The data used is recycled from previous information attached to your old advertising. This includes information from apps, customer files, engagement and offline activity. Anything that has a metric attached to an individual can be used with Facebook retargeting.
How Can It Help Your Business?
There will always be missed opportunities before, during and after a marketing campaign. Reinvesting the data gained from the previous campaign prevents you from starting completely over. Instead of starting from scratch, you’ll gain a clear insight into what gets consumers to cross the finish line at checkout. Retargeting is meant to be a powerful tool that thrives on previous data that would otherwise go unnoticed.
Retention comes into play, but doesn’t make up the entire story of retargeting on Facebook. You can run a retargeting campaign and only look into new consumers. It’s flexible, and meant to enhance your business based on your specific needs.
The Different Types of Retargeting
The two main types of Facebook retargeting are list-based and pixel-based. Each serves a purpose, with their own specific pros and cons.
List-based retargeting is a limited but fascinating concept. It uses the data you already have on hand to create a specialized list that Facebook uses to show ads. This method works on many of the major social media platforms, but has shown significant advantages on Facebook. Since list-based targeting uses email lists as its base, companies are at the mercy of that particular resource. An outdated or inaccurate email list will lead to low quality retargeting efforts.
When relying on list-based retargeting, a larger email list is not always a guaranteed win for a company.
Upselling and Cross Selling
Even when the customer is happy, proving the value of an upsell is an ongoing process. This led to a rise in cross selling, but was only beneficial to companies that had the resources. As you reconnect with old and new customers, upselling or cross selling becomes part of the closing process.
Both methods are difficult, but become trivial once you have the data to back up your new campaign. Most companies see an increase in profits in a short amount of time. This makes Facebook retargeting a valuable way to test drive upselling and cross selling methods.
Brand awareness is the golden goose that all businesses constantly chase. Once you have a notable brand, it becomes the identity of your entire company. Protecting the brand is important, and sometimes entire marketing campaigns are launch to reinvigorate the company image. So, how does Facebook retargeting work its way into this?
Facebook lookalike audiences became a thing when companies wanted to reach new customers with similar interests and habits as their current best customers. Creating a lead that finds this new audience is possible when brand awareness reaches its peak. If you want to keep brand awareness high, then Facebook retargeting does all of the tough work while increasing your reach to new consumers.
Conversions are hard to pull off without a specific time investment. All of that goes to waste if you’re not positioning yourself to use previous data to convert customers. No matter how visitors arrive to your website, their presence is proof that there is an interest to purchase a product or service.
If they leave without making a purchase, it’s up to you to figure out why. A lost sale is not the same as losing a customer. Being able to convert that customer into an actual sale is a major strength of retargeting. And even if it’s unsuccessful, you’ll be able to use the additional data to convert another customer.
Influence Buying Decisions
When a consumer becomes firm in their buying decision, then your influence gains a significant bump. At this point your retargeting is directly influencing the buying decisions of individuals or groups. You’ll see a visual representation of this with online feedback and testimonials. All of the positive information provided comes from consumers that are satisfied with the entire sales experience.
Even the negative feedback plays a role, and can serve as the proof you need to reuse data to improve a weak point in your marketing. When a company puts effort into retargeting their ads, they gain monumental increases in customer conversions, ad recognition, clicks, sales and branded searches.
Remarketing Vs. Retargeting
Learn the difference between retargeting and remarketing. Retargeting gains the attention of interested customers that never purchased your products or services. Remarketing leans more towards gaining the attention of inactive or lost customers. Don’t make the mistake of running a retargeting campaign when remarketing would work better. The good news is that the data used from one is still essential for the other. An email list with decent accuracy can be a valuable asset for remarketing or retargeting.
Making the Right Choice
Facebook retargeting should be a priority with how you manage your data collection. Embracing its use will optimize the most important part of your business. Once you get the hang of things, your ROI will reach a whole new level.
7 Data Collection Tools Every Company Must Have
Businesses need to use their knowledge and data effectively if they want to succeed. This involves collecting plenty of information to ensure your business can make changes and adjustments based on various trends. If you need help collecting and organizing your data, you should get these seven data collection tools to help your company.
Landing Page Tools
Many companies will create landing pages for their businesses. Landing pages are specific spots on your website where people can create accounts, get email newsletters and receive various updates. However, collecting that information on your own will be difficult at times, so you may want a tool to assist you.
Landing page tools will automatically collect that information from your landing pages. On top of that, the tools will automatically add those email addresses and phone numbers to the corresponding newsletters and text services. This makes landing pages ideal if you want to build your text or email channels.
By using this tool, you won’t have to organize the information on your own.
While data collection remains an important part of the industry, you will also need an effective way to analyze data. This makes data catalog tools an important aspect of your business. As you utilize tools like these, you can figure out what the data means to make informed decisions for your business.
For example, data catalog tools can make your data straightforward and simple to understand if you struggle with analyzing it. It can also help you sort through data to ensure you find information that pertains to you. As you focus on this data and use it, you can assist your business, making data collection tools ideal for most situations.
Data collection also involves finding more ways for your business to collect useful data. Sure, you could pay to get various emails, but most of those people will get mad at your business for contacting them. This makes referral programs a great option since you can have customers invite their friends to see your business.
Referral programs are great for data collection since your customers can find people for you. From there, the referral tools will collect the data for you, allowing you to send deals and other information to those referrals. Many of these tools will handle referral contacting automatically, so you can boost your sales without putting in any extra work.
Referral programs allow you to work with your customers as you find new ones.
When you work with customers, you need to figure out what they think about your business. That way, you can make changes based on what they suggest, allowing you to better appeal to your customers. This makes feedback collection a great way to see what your customers expect from you as a business.
When you focus on feedback collection, you can find out what your customers like and dislike about your business. You can receive feedback in multiple ways, such as through reviews or surveys. Feedback collection tools will automatically gather that information for you, so you can easily view it whenever you want to see what you should change.
Search engine optimization (SEO) involves your business identifying what keywords people look up online when they want to find information about your business. For example, if you sell computers in Alaska, you will want words like computer, store and Alaska associated with your business. That way, you will appear in those online searches.
SEO involves more than just putting keywords into your website. You also need to identify those keywords, use them effectively and include links that will improve your SEO. Doing this on your own requires tons of effort and time, so you should look for SEO tools to simplify the process and make it easier on you.
If your business has an online presence, you need to look into SEO tools.
When your business makes sales, you can use transaction tracking tools to gather that information for you. For example, you can use these tools to let you know which of your products sell, how often you get sales and when people tend to buy them. This will help you understand your customers’ behavior.
For example, if you notice some of your products tend to sell better than others, you can see what your customers find appealing about those products. This gives you the perfect opportunity to make adjustments to your other products, allowing you to draw in more customers and improve your business overall.
Speaking about these tools, it’s also important to understand what types of customers buy from you. Demographics include a variety of points, such as age, where they live, their jobs and lots of other information. By going off the demographics, you can find out what types of people like your products, allowing you to see what you can do to further appeal to them.
For example, if you notice you get lots of younger customers, you can look into trends that they like. Some of your demographics may let you know you should use more technology, though this all depends on the types of customers you get. By using demographic tools, you can quickly find out this information and make decisions based on it.
Demographic tools are key if you want to better understand your customers and what they want from you.
Since data collection plays a major role in the success of companies, you should look into the tools available. Make sure you check these seven tools in particular to assist your business. As you focus on data collection and utilize these tools, you can help your business save time and money.
Deepfake detectors and datasets exhibit racial and gender bias, USC study shows
Some experts have expressed concern that machine learning tools could be used to create deepfakes, or videos that take a person in an existing video and replace them with someone else’s likeness. The fear is that these fakes might be used to do things like sway opinion during an election or implicate a person in a crime. Already, deepfakes have been abused to generate pornographic material of actors and defraud a major energy producer.
Fortunately, efforts are underway to develop automated methods to detect deepfakes. Facebook — along with Amazon and Microsoft, among others — spearheaded the Deepfake Detection Challenge, which ended last June. The challenge’s launch came after the release of a large corpus of visual deepfakes produced in collaboration with Jigsaw, Google’s internal technology incubator, which was incorporated into a benchmark made freely available to researchers for synthetic video detection system development. More recently, Microsoft launched its own deepfake-combating solution in Video Authenticator, a system that can analyze a still photo or video to provide a score for its level of confidence that the media hasn’t been artificially manipulated.
But according to researchers at the University of Southern California, some of the datasets used to train deepfake detection systems might underrepresent people of a certain gender or with specific skin colors. This bias can be amplified in deepfake detectors, the coauthors say, with some detectors showing up to a 10.7% difference in error rate depending on the racial group.
Biased deepfake detectors
The results, while surprising, are in line with previous research showing that computer vision models are susceptible to harmful, pervasive prejudice. A paper last fall by University of Colorado, Boulder researchers demonstrated that AI from Amazon, Clarifai, Microsoft, and others maintained accuracy rates above 95% for cisgender men and women but misidentified trans men as women 38% of the time. Independent benchmarks of major vendors’ systems by the Gender Shades project and the National Institute of Standards and Technology (NIST) have demonstrated that facial recognition technology exhibits racial and gender bias and have suggested that current facial recognition programs can be wildly inaccurate, misclassifying people upwards of 96% of the time.
The University of Southern California group looked a three deepfake detection models with “proven success in detecting deepfake videos.” All were trained on the FaceForensics++ dataset, which is commonly used for deepfake detectors, as well as corpora including Google’s DeepfakeDetection, CelebDF, and DeeperForensics-1.0.
In a benchmark test, the researchers found that all of the detectors performed worst on videos with darker Black faces, especially male Black faces. Videos with female Asian faces had the highest accuracy, but depending on the dataset, the detectors also performed well on Caucasian (particularly male) and Indian faces. .
According to the researchers, the deepfake detection datasets were “strongly” imbalanced in terms of gender and racial groups, with FaceForensics++ sample videos showing over 58% (mostly white) women compared with 41.7% men. Less than 5% of the real videos showed Black or Indian people, and the datasets contained “irregular swaps,” where a person’s face was swapped onto another person of a different race or gender.
These irregular swaps, while intended to mitigate bias, are in fact to blame for at least a portion of the bias in the detectors, the coauthors hypothesize. Trained on the datasets, the detectors learned correlations between fakeness and, for example, Asian facial features. One corpus used Asian faces as foreground faces swapped onto female Caucasian faces and female Hispanic faces.
“In a real-world scenario, facial profiles of female Asian or female African are 1.5 to 3 times more likely to be mistakenly labeled as fake than profiles of the male Caucasian … The proportion of real subjects mistakenly identified as fake can be much larger for female subjects than male subjects,” the researchers wrote.
The findings are a stark reminder that even the “best” AI systems aren’t necessarily flawless. As the coauthors note, at least one deepfake detector in the study achieved 90.1% accuracy on a test dataset, a metric that conceals the biases within.
“[U]sing a single performance metrics such as … detection accuracy over the entire dataset is not enough to justify massive commercial rollouts of deepfake detectors,” the researchers wrote. “As deepfakes become more pervasive, there is a growing reliance on automated systems to combat deepfakes. We argue that practitioners should investigate all societal aspects and consequences of these high impact systems.”
The research is especially timely in light of growth in the commercial deepfake video detection market. Amsterdam-based Deeptrace Labs offers a suite of monitoring products that purport to classify deepfakes uploaded on social media, video hosting platforms, and disinformation networks. Dessa has proposed techniques for improving deepfake detectors trained on data sets of manipulated videos. And Truepic raised an $8 million funding round in July 2018 for its video and photo deepfake detection services. In December 2018, the company acquired another deepfake “detection-as-a-service” startup — Fourandsix — whose fake image detector was licensed by DARPA.
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U.S. listing ban on Luokung lifted after judge’s decision
By Karen Freifeld
WASHINGTON (Reuters) – Nasdaq Inc has withdrawn a decision to delist the shares of Luokung Technology Corp, the Chinese mapping technology company said on Thursday, after a U.S. judge suspended an imminent investment ban imposed under the Trump administration.
The ruling and listing news sent shares of the company nearly 20% higher. Luokung issued a news release on Thursday saying Nasdaq notified the company it has withdrawn its delisting letter and shares would continue to trade on the market, not be suspended on May 7. A Nasdaq spokesman declined to comment.
A spokesman for the U.S. Department of Justice did not immediately respond to a request for comment.
Luokung is the second company on a U.S. list of alleged Communist Chinese military companies subject to an investment ban to win a preliminary injunction halting the designation.
U.S. District Court Judge Rudolph Contreras in Washington issued a similar order in March in favor of Beijing-based smartphone maker Xiaomi Corp.
In granting an injunction in the case brought by Luokung challenging the ban, Contreras said the U.S. Department of Defense’s designation process was flawed.
“Many of the associations the Department of Defense seemed most troubled by – such as Luokung’s purported forays into space systems or its potential future contracts with the Chinese National Geospatial Information Center … do not appear to have materialized, nor are they likely to bear fruit before this case can be decided on the merits,” the judge wrote in his decision.
He added the government has not identified a single technology transfer from Luokung to the People’s Republic of China.
More than 40 companies were added to a list of U.S. companies subject to the investment ban in the waning days of the Trump administration.
(Reporting by Karen Freifeld in New York; Editing by Matthew Lewis)
Image Credit: Reuters
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