Earlier in 2019, Jessica (not her real name) had “spent” over $15,000 on Amazon for over 700 products – all as a dishonest side hustle.
In the back corner of Jessica’s two-family home dining room is a table with un-reviewed Amazon products on top and up-for-grabs underneath. Jessica’s routine goes a little like this: her boyfriend brings in Amazon packages from their front porch, she takes photographs of the products from the boxes, and while watching TV, she posts thoughtful 5-star reviews (some complete with photos and a video) on the Amazon products.
Full Of Everything
In her home are the most random finds, from Halloween decorations and common house items to more faux leather purses, earbuds, and queen-size inflatable mattresses than humanly needed. Although most are cheaply made, Jessica has given each a 5-star review and proudly points to the items as ones that she “would have had to pay for”. Not that she does a faulty job at quality assurance, but that those companies that sell on Amazon send her products and commission her for the purchases.
What Jessica (treacherously) does helps sellers who are seeking growth in their businesses in two ways: by bolstering their reviews and by elevating their items’ sales ranks against similar listings.
To make it to Amazon’s massive scale of inscrutable algorithms and fickle rules, third-party sellers can easily get away with a reliable cheat to get good reviews and a high search ranking – through bribery.
Reaching out to Jessica and other review groups with thousands of dedicated members through Facebook, WhatsApp, or email means giving a specific set of instructions to follow to closely simulate a legitimate-looking Amazon purchase that typically goes like this: look up specific, company-provided keywords to elevate the product’s profile in the search results; purchase a certain product using, in Jessica’s case, her Amazon Prime member account and Amazon Chase credit card, to be labeled as a “verified purchase”; and leave a 5-star review after a calculated amount of time, at least five to six days upon receipt of the items.
The sellers then reimburse the expenses incurred outside Amazon, perhaps via PayPal or with an Amazon gift card, and allow the reviewers’ possession of the items.
Since Jessica’s credit card is an Amazon-branded rewards card, she also gets extra bonuses for her purchases. Looking at it this way, third-party sellers aren’t the only ones reimbursing her fake reviews – Amazon is in on it, too.
On BuzzFeed News’ query on how Amazon policies inauthentic reviews, the Amazon spokesperson noted that over 13 million bogus review attempts had been prevented and that it responded accordingly against more than 5 million accounts of review-manipulating sellers. Sly third-party sellers, though, still continue to exploit the blind spots in the system and get away unharmed.
Most Amazon products aren’t really sold by Amazon itself, but by independent third-party sellers. If you ever come across a product with a “Sold by (Store Name) and Fulfilled by Amazon” written below the price, these are merchandisers who get a cut in the sales with their access to Amazon’s customers. In 2018, Amazon recorded a $160-billion spending by customers on items from third-party sellers – an impressive 58% portion of all the site’s sales, which Amazon CEO Jeff Bezos doesn’t find delightful.
With thousands of new sellers on the site each day, a Wall Street Journal story in 2018 reported that in every 1/50th of a second, a new product from China was uploaded. As an appeal, comments from these Chinese sellers like, “This will help our small business out a lot!” and “We’re a small family business selling on Amazon” draw in the empathy of reviewers to the small business owners.
This scheme on Amazon’s algorithm gives people the illusion that a product has a crazy sales velocity, making it more search-visible. In addition to reviews largely impacting listings, search ranking also depends on the frequency of sales. The higher the ranking, the bigger the sellers’ monthly revenue.
In Jessica’s experience in fake reviewing, she has rated almost every item five stars, except for a cross-body purse that shipped with a broken magnet, which she rated a three to eventually receive a $20 refund from the seller. Although sometimes there are awful products, they still get the same 5-star rating; she just doesn’t trust health and beauty products on Amazon for her chemist boyfriend’s warnings on toxic ingredients in unregulated products.
Diminishing Returns For The Rest Of Us
This involvement in product reviewing was enough to give Jessica the exposure to the existence of inauthentic Amazon reviews. For other customers, however, the trouble of discerning commissioned reviews from genuine ones becomes present, all while unsuspecting patrons become prey to cheap products with inflated ratings. This display of fraud then diminishes the factor of trust in the site.
Attracting genuine customers to a listing could cost excessive amounts of money without the guarantee of good reviews. To solve this, some desperate sellers engage in treachery. Pattern chief revenue officer John LeBaron inferred, “The cheapest, easiest way to make money is to cheat.”
What Is REAL Messenger?
REAL Messenger is an app designed to help agents promote their listings and themselves.
Social media is table stakes now. But not all apps are created equal. Facebook is for friends. Instagram is for interests and LinkedIn is for work connections. So why shouldn’t real estate has its own social media platform that brings together a global community of agents, buyers, and sellers?
We’ve learned that there’s power in a platform to find what you’re looking for, to share, and to chat — opening doors and elevating your presence. We’ve watched how people become influencers with followers who devour every post. These influencers don’t have to pay to promote themselves, they’ve built their audience through content, personality, and what they stand for.
In contrast, real estate has become “pay-to-play,” restricting agents from showcasing their listings on well-known real estate platforms, unless they pay (a lot!) to promote them. Agents have lost control in the real estate process, while big proptech profits from agents’ hard-earned listings.
Social media meets real estate
Imagine a social media outlet just for real estate — one like Instagram or WhatsApp geared 100% to our industry. The audience is engaged in real estate. Agents connect to share information about listings with one another or potential buyers and sellers – and retain those connections. Agents can even share their knowledge of properties before they are listed publicly.
It’s now all possible with the REAL Messenger app, an incredibly fast social media platform for real estate agents to promote their listings and share their styles and specialties, as well as their sales history and approach. Integrated into the app is an easy chat feature that replaces the need for cold calls, excessive emails, and online ads that don’t yield much return on investment.
Giving agents back control
From the agents’ perspective, sites and apps like Zillow are taking listing information from MLS agreements, repackaging it to promote it on their sites, then selling the information back to the agents who owned it in the first place! Agents end up paying these sites expensive advertising fees. And while many agents use Instagram to let their followers know about their listings, they are not really targeting a real estate-specific audience. WhatsApp is also used for secure, data-encrypted conversations ensuring quick exchanges of information. But how can an agent build their business when they’re promoting to people who are not in the market? Our formidable team of developers created the best of all worlds for the world of real estate — it’s like Instagram with a secure chat feature similar to that of WhatsApp.
The REAL advantage
Self-branding and inbound marketing is built into REAL. Agents brand themselves by creating content that showcases their listings, providing information buyers will need, and sharing their successes with transactions. Agents use the app’s three-point rating system to describe their listing and other important characteristics.
Potential buyers can search for anything specific to their interests (i.e., a home with a patio, a garden, or a swimming pool) in particular zip codes. They can also browse by scrolling through the listings to find the hottest, most popular real estate properties in their areas. These potential buyers can follow agents whose posts resonate with their preferences and interests, expanding agents’ networks.
Deepfakes are fake videos created using digital software, machine learning, and face swapping. Deepfakes are computer-created artificial videos in which images are combined to create new footage that depicts events, statements, or actions that never actually happened. The results can be quite convincing. Deep fakes differ from other forms of false information by being very difficult to identify as false.
How do deepfakes work?
The basic concept behind the technology is facial recognition, users of Snapchat will be familiar with the face swap or filter functions which apply transformations or augment their facial features. Deep Fakes are similar but much more realistic. Fake videos can be created using a machine learning technique called a “generative adversarial network” or GAN. For example, a GAN can look at thousands of photos of Beyonce and produce a new image that approximates those photos without being an exact copy of any one of the photos. GAN can be used to generate new audio from existing audio, or new text from the existing text – it is a multi-use technology. The technology used to create Deep Fakes is programmed to map faces according to “landmark” points. These are features like the corners of your eyes and mouth, your nostrils, and the contour of your jawline.
When seeing is no longer believing
While the technology used to create deep fakes is a relatively new technology, it is advancing quickly and it is becoming more and more difficult to check if a video is real or not. Developments in these kinds of technologies have obvious social, moral, and political implications. There are already issues around news sources and the credibility of stories online, deep fakes have the potential to exacerbate the problem of false information online or disrupt and undermine the credibility of and trust in news, and information in general.
The real potential danger of false information and deep fake technology is creating mistrust or apathy in people about what we see or hear online. If everything could be fake does that mean that nothing is real anymore? For as long as we have had photographs and video and audio footage they have helped us learn about our past and shaped how we see and know things. Some people already question the facts around events that unquestionably happened, like the Holocaust, the moon landing, and 9/11, despite video proof. If deepfakes make people believe they can’t trust video, the problems of false information and conspiracy theories could get worse.
False news can lead to false memories
One of the most common concerns and potential dangers of deep fakes and false information, in general, is the impact it can have on democratic processes and elections.
A recent survey from UCC confirmed that people recall fake news more than real news. The results of the survey indicated that voters may form false memories after seeing fabricated news stories, especially if those stories align with their political beliefs, according to a new study. The researchers suggest the findings indicate how voters may be influenced in upcoming political contests, like the 2020 US presidential race.
The author of the report Dr. Gillian Murphy added; “This demonstrates the ease with which we can plant these entirely fabricated memories, despite this voter suspicion and even despite an explicit warning that they may have been shown fake news,”.
What Is Cognitive Computing?
Cognitive computing is the use of computerized models to simulate the human thought process in complex situations where the answers may be ambiguous and uncertain. The phrase is closely associated with IBM’s cognitive computer system, Watson.
Computers are faster than humans at processing and calculating, but they have yet to master some tasks, such as understanding natural language and recognizing objects in an image. Cognitive computing is an attempt to have computers mimic the way a human brain works.
To accomplish this, cognitive computing makes use of artificial intelligence (AI) and other underlying technologies, including the following:
- Expert systems
- Neural networks
- Machine learning
- Deep learning
- Natural language processing (NLP)
- Speech recognition
- Object recognition
Cognitive computing uses these processes in conjunction with self-learning algorithms, data analysis, and pattern recognition to teach computing systems. The learning technology can be used for speech recognition, sentiment analysis, risk assessments, face detection, and more. In addition, it is particularly useful in fields such as healthcare, banking, finance, and retail.
How Does Cognitive Computing Work?
Systems used in the cognitive sciences combine data from various sources while weighing context and conflicting evidence to suggest the best possible answers. To achieve this, cognitive systems include self-learning technologies that use data mining, pattern recognition, and NLP to mimic human intelligence.
Using computer systems to solve the types of problems that humans are typically tasked with requires vast amounts of structured and unstructured data fed to machine learning algorithms. Over time, cognitive systems are able to refine the way they identify patterns and the way they process data. They become capable of anticipating new problems and modeling possible solutions.
For example, by storing thousands of pictures of dogs in a database, an AI system can be taught how to identify pictures of dogs. The more data a system is exposed to, the more it is able to learn and the more accurate it becomes over time.
To achieve those capabilities, cognitive computing systems must have the following attributes:
- Adaptive. These systems must be flexible enough to learn as information changes and as goals evolve. They must digest dynamic data in real time and adjust as the data and environment change.
- Interactive. Human-computer interaction is a critical component of cognitive systems. Users must be able to interact with cognitive machines and define their needs as those needs change. The technologies must also be able to interact with other processors, devices, and cloud platforms.
- Iterative and stateful. Cognitive computing technologies can ask questions and pull in additional data to identify or clarify a problem. They must be stateful in that they keep information about similar situations that have previously occurred.
- Contextual. Understanding context is critical in thought processes. Cognitive systems must understand, identify and mine contextual data, such as syntax, time, location, domain, requirements, and a user’s profile, tasks, and goals. The systems may draw on multiple sources of information, including structured and unstructured data and visual, auditory, and sensor data.
Examples and applications of cognitive computing
Cognitive computing systems are typically used to accomplish tasks that require the parsing of large amounts of data. For example, in computer science, cognitive computing aids in big data analytics, identifying trends and patterns, understanding human language, and interacting with customers.
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