Sourced from Brighter Side of News

A study in which machine-learning models were trained to assess over 1 million companies has shown that artificial intelligence (AI) can accurately determine whether a startup firm will fail or become successful. The outcome is a tool (www.venhound.com) that has the potential to help investors identify the next unicorn.

It is well known that around 90% of startups are unsuccessful: between 10% and 22% fail within their first year, and this presents a significant risk to Venture Capitalists and other investors in early-stage companies. In a bid to identify which companies are more likely to succeed, researchers have developed machine-learning models trained on the historical performance of over 1 million companies. Their results, published in KeAi’s The Journal of Finance and Data Science, show that these models can predict the outcome of a company with up to 90% accuracy. This means that potentially 9 out of 10 companies are correctly assessed.

“This research shows how ensembles of non-linear machine-learning models applied to big data have huge potential to map large feature sets to business outcomes, something that is unachievable with traditional linear regression models,” explains co-author Sanjiv Das, Professor of Finance and Data Science at Santa Clara University’s Leavey School of Business in the US.

The authors developed a novel ensemble of models in which the combined contribution of the models outweighs the predictive potential of each one alone. Each model classifies a company, placing it in one of several success categories or a failure category with a specific probability. For example, a company might be very likely to succeed if the ensemble says it has a 75% probability of being in the IPO (listed on the stock exchange) or ‘acquired by another company’ category, while only 25% of its prediction would fall into the failed category.

Credit must be given to the creator. Only noncommercial uses of the work are permitted. No derivatives or adaptations of the work are permitted. (CREDIT: Greg Ross)

The researchers trained the models on data sourced from Crunchbase, a crowd-sourced platform containing detailed information on many companies. They married the Crunchbase observations with patent data from the USPTO (United States Patent and Trademark Office). Given the crowd-sourced nature of Crunchbase, it was no surprise to learn that some companies’ entries miss information. This observation inspired the authors to measure the amount of information missing for each company and use this value as an input to the model. This observation turned out to be one of the most critical features in determining whether a company would be acquired or otherwise fail.

Lead author Greg Ross of Venhound Inc. notes that the ensemble of models, along with novel data features, “generates a level of accuracy, precision and recall that exceeds other similar studies. Investors can use this to quickly evaluate prospects, raise potential red flags and make more informed decisions on the composition of their portfolios.”

Feature Image Credit: Creative Commons

Sourced from Brighter Side of News

By Ryan Barwick

Chris Lu, cofounder and chief technology officer of Copy.ai, told us these tools give copywriters a “first draft” to work with.

“Marketing Brew is the home of provocative ideas, fresh thinking, provocative insights, and interesting perspectives on what they think about marketing, media and advertising. This publication gives you a way to digest their news, quick takes and new offerings in the field.”

This description of our newsletter was written by artificial intelligence. Pretty close, right?

Given only our name and a brief description—“a newsletter about marketing, media and advertising”—a tool called Copy.ai was able to spit out that paragraph.

The tool is part of a wave of smart content-churning machines that use the power of artificial intelligence to steal writing jobs make life easier for whomever’s crunching copy.

HAL meets Stan Freberg

Copy.ai and other AI-enabled copywriting companies like Jarvis and Copysmith are built upon OpenAI’s GPT-3.

According to the smart folks over at Emerging Tech Brew, GPT-3 is kind of a big deal. Trained on roughly a trillion words to predict—but not understand—text, it’s widely considered to be among the most advanced language models in existence.

“Large language models are powerful machine learning algorithms with one key job description: identifying large-scale patterns in text. The models use those patterns to ‘parrot’ human-like language. And they quietly underpin services like Google Search—used by billions of people worldwide—and predictive text software, such as Grammarly,” writes Emerging Tech Brew’s Hayden Field.

Of course, if you’re a marketer, who cares? You just need content. And lots of it. That’s where these tools come in handy. They can help write everything from Instagram captions to product descriptions to blog posts.

“We want to humanize AI. We want to help you start from something, and not a blank slate,” Copysmith CEO Shegun Otulana told Marketing Brew.

There’s an assumption that machines could take jobs away from writers, but Otulana doesn’t see it quite that way. “There’s an aspect of writing that isn’t easily replaced. A computer can’t tap into the human interactions you express in a story, the emotional aspects of a story you tease out. A computer can’t live the life of a human.”

But if you’re a writer who specializes in, say, product descriptions for e-commerce sites—or other types of copy that aren’t exactly trying to forge a human connection—these tools might pose more of a threat, he said.

For copywriters who need to bang out posts on multiple platforms like Instagram, Facebook, and LinkedIn, there’s a volume benefit to these tools, since the AI provides various options for each post. Sure, you might not actually need dozens of call-to-action phrases, but any professional could whittle those down to five good ones and tweak them if needed.

“We try to give you the first draft,” Chris Lu, cofounder and chief technology officer of Copy.ai, told us. “Content creation is a treadmill. You’ve got to make a lot of it, it has to be relevant, and it has to be on brand.”

Andrew Race, a strategist at the digital agency Juice, called Copy.ai a time saver. In five minutes, I’ve got 25 different variations [of a headline]. Before, I would have had to write every single headline or idea out myself,” he said. He estimates it cuts his actual copywriting time in half.

  • So far Copy.ai, which debuted in October 2020, has 4,100 paying subscribers made up mostly of marketers.

Ryan’s artificial copy

We asked Copy.ai to make a couple of Facebook headlines and posts for a made-up brand called Ryan’s Lemonade, giving only the keywords “lemonade, sweet, tart, fresh.”

Here are three versions it came up with:

  • It’s So Good You’ll Want Seconds: With a history of providing quality fruit drinks, Ryan’s continues on with their new line of lemonades. From sweet and tart to a hint of fresh, Ryan’s is bound to quench any thirst.
  • Cool, Refreshing: Pure and natural lemonade sweetened only with real sugar, not high fructose corn syrup.
  • Nothing is as refreshing as Ryan’s: Have you ever felt like you were in paradise? Everyday life is a bit harder than we wish it to be. Lemon Ryan’s is the perfect amount of all things good, made by real people in the sun-drenched hills of California. $5 for a gallon? No… lets make that two!

As you can see, it isn’t perfect. But it’s pretty good, and each took less than 10 seconds. Copy.ai assumes a lot—we’re not sure where “$5” or “California” came from. “These are natural language models. All it’s trying to do is write text. It’s just trying to predict the next word,” Lu told us.

If only a few keywords are plugged in, the content produced by these tools can have a generic feel to it. Lu said he doesn’t expect plagiarism to be an issue, but did say the company is planning on adding a plagiarism detector. “The AI helps with how to say, not what to say,” he added.

Copywriter –> AI editor

Similar to Copy.ai, Jarvis also lists templates for platforms like Facebook and LinkedIn, but its bread and butter is longer-form content, like a blog post.

According to Dave Rogenmoser, founder of Jarvis, it has nearly 30,000 paying subscribers. At least 60–70% of its clients, which include Airbnb, Zillow, and CVS, are using it for projects “around 500 words,” Rogenmoser told Marketing Brew.

By inputting a few key words, Jarvis can spit out entire paragraphs, turning your average copywriter into an editor, who can guide the machine in a specific direction. If it veers off and becomes illegible, a user has to delete what isn’t working and try again. It doesn’t eliminate work entirely, as someone still has to pick and choose what works.

  • This post by Danny Veiga, a digital marketer in San Antonio, was written by Jarvis. Veiga told Marketing Brew Jarvis did about 80% of the work. The other 20% was mostly fact checking.
  • Veiga uses Jarvis for his email marketing, social posts, and homepage copy.

“Jarvis thrives when you need to write a lot of words, but they don’t need to be the most important words you’ve ever written,” said Rogenmoser. In other words, AI probably won’t win a Pulitzer anytime soon, but if you’re cranking out copy, it’ll give you a template for a flood of usable jumping points.

“It takes the mental load off. Writers are safe,” said Rogenmoser. For now.

By Ryan Barwick

Sourced from Morning Brew

Artificial Intelligence (AI) mimics the cognitive functions of the human mind, particularly in learning and problem-solving. Many of the apps that we use today are powered by AI. From voice-activated virtual assistants to e-commerce, AI applications are everywhere.

With the advancements in AI technology and access to big data, companies across different industries are integrating AI into their processes to find solutions to complex business problems.

The application of AI is most noticeable within the retail and e-commerce space. Websites and apps can interact intelligently with customers, creating a personalized approach that enhances the customer experience.

No matter what industry your business operates in, these seven tips can help you acquire and retain customers more efficiently at a fraction of the time it takes to do things manually.

How to Use AI to Get and Keep Customers

1. Identify Gaps in Your Content Marketing Strategy

If you’re just starting with content marketing, you’ll need to know what type of content to create.

By using AI, you can identify the gaps, find fixes, and evaluate the performance of your content marketing campaign.

Take Packlane, a company that specializes in custom package designs, for example. They came up with high-quality content like helpful blog posts that provide valuable information. At the same time, the content they publish makes it easier for their target market to understand their brand and services.

If you’re in the retail or e-commerce space, you can use AI to identify the gaps in your content marketing. Your content may be focused on your products and their features, but through AI, you can determine the relevant content that addresses your audience’s needs and pain points.

2. Pre-Qualify Prospects and Leads

Not every visitor to your site will become a paying customer. If you’re not getting sales despite the massive traffic, it means you’re generating low-quality leads.

Some reasons why this happens includes:

  • Targeting the wrong audience
  • Poor content marketing strategy
  • Using the wrong type of signup form
  • Promoting in the wrong social media platforms
  • Ineffective calls to action

These explain why 80% of new leads never convert into sales. The mistakes can be rectified with the help of artificial intelligence.

AI tools can extract relevant data to help you learn more about your target audience. These tools also provide predictive analytics on your customers’ behaviour. They, in turn, help improve your lead generation strategy because you’ll know which leads to pursue, where to find them, and how to effectively engage them.

3. Provide Personal Recommendations

According to a report by the Harvard Business Review, even though there are privacy concerns when consumers’ personal information changes hands, people still value personalized marketing experiences.

Brands that tailor their recommendations based on consumer data boost their sales by 10% over brands that don’t.

Recommendation systems’ algorithms typically rely on data on browsing history, pages visited, and previous purchases. But AI is so advanced that it can analyse customers’ interactions with the site content and find relevant products that will interest the individual customer. This way, AI makes it easier to target potential customers and effectively puts the best products in front of the site visitors.

Because of AI, recommendation engines are able to filter and customize the product recommendations based on each customer’s preferences. It’s a cycle of collecting, storing, analysing, and filtering the available data until it matches the customers’ preferences.

This is an effective way of acquiring and retaining customers because there’s an element of personalization.

4. Reduce Cart Abandonment

A high cart abandonment rate is the bane of e-commerce business owners. According to a study by the Baymard Institute, online shopping cart abandonment rate is close to 70%.

Users abandon their online carts for various reasons:

  • high extra costs
  • complicated checkout process
  • privacy concerns
  • not enough payment methods, or
  • they’re not ready to buy yet.

Using AI-powered chatbots is one way to reduce cart abandonment. AI chatbots can guide the customers through their shopping journey.

AI chatbots can have a conversational approach and give the customer a nudge to prompt them to complete the purchase. These chatbots can also act as a virtual shopping assistant or concierge that can let a customer know about an on-the-spot discount, a time-sensitive deal, a free shipping coupon, or any other incentives that will encourage them to complete the checkout.

With AI, lost orders due to cart abandonment are recoverable and can lead to an increase in conversion rate for e-commerce businesses.

5. Increase Repurchases With Predictive Analytics

Predictive analytics is the process of making predictions based on historical data using data mining, statistical modelling, artificial intelligence, machine learning, and other techniques. It can generate insights, forecast trends, and predict behaviours based on past and current data.

In marketing, predictive analytics can be used to predict customers’ propensity to repurchase products as well as its frequency. When used to optimize marketing campaigns, AI-powered predictive analytics can generate customer response, increase repurchase, and promote cross-selling of relevant products.

It’s all part of the hyper personalized marketing approach, where brands interact and engage with customers and improve their experience by anticipating their needs and exceeding their expectations.

With predictive analytics, you can focus your marketing resources on customer retention and targeting a highly motivated segment of your market that are more than happy to return and repurchase your products. This approach is less expensive than advertising or implementing pay-per-click campaigns.

6. Improve Your Website User Experience

Every business—big or small—knows the importance of having a website, where visitors can interact with the brand, respond to a call to action, or purchase products. But it’s not enough to just have an online presence; it’s important that visitors to the site have a great experience while navigating through your site.

What makes for a great user experience? Users have different expectations. Some of them want faster loading time, while others want a simple and intuitive interface. But most important of all, they want to find what they’re looking for. It could be a product, content, or a solution to a problem. Whatever they may be, it’s up to you to meet their expectations.

With artificial intelligence, you can improve your website user experience tenfold. Here are some of the ways AI can be used to improve user experience.

Search relevance

This pertains to how accurate the search results are in relation to the search query.  The more relevant the results are, the better search experience the users will have. This means they are likely to find relevant content answering their queries or finding products that solve their problems.

Personalized recommendations

Content that is tailor-made for the user tends to have greater engagement which increases the likelihood of conversation. Amazon has perfected the product recommendation system using advanced AI and machine learning. AI gets data from customers and uses it to gain insights and apply predictive analysis to recommend relevant products for cross-selling opportunities.

AI chatbots

The presence of chatbots contributes to a great user experience because they provide 24/7 assistance and support in the absence of human customer service.  Users can get accurate answers to their inquiries quickly and efficiently, compared to scrolling through a text-based FAQs.

7. Social Listening for Potential Customers

Social listening is the process of analysing the conversations, trends, and buzz surrounding your brand across different social media platforms. It’s the next step to monitoring and tracking the social media mentions of your brand and products, hashtags, industry trends, as well as your competitors.

Social listening analyses what’s behind the metrics and the numbers. It determines the social media sentiment about your brand and everything that relates to it. It helps you understand how people feel about your brand. All the data and information you get through social listening can be used to guide you in your strategy to gain new customers.

Social media monitoring and listening can be done much more efficiently with the help of artificial intelligence. It’s an enormous task for a team of human beings to monitor and analyse data, but with AI-powered social media tools, all the tedious tasks can be automated. They can be trained to leverage data to provide valuable insights about your brand with high accuracy.

With AI and machine learning, your social listening can easily determine your audience, brand sentiments, shopping behaviour, and other important insights. By having this information within reach, you’ll know how you can connect with them more effectively and turn them from prospects to paying customers.

Key Takeaways/Conclusion

More companies across different industries are using the power of artificial intelligence and machine learning to significantly increase brand awareness, enhance customer engagement, improve user experience, and meet customer expectations.

  • AI can identify gaps in your content marketing strategy so that you can create content that’s relevant to your target audience.
  • AI can help you generate high-quality leads that are likely to buy your products.
  • With AI, you can personalize and tailor-fit your product recommendations based on your customers’ preferences, increasing repeat purchases.
  • AI can be integrated into your e-commerce site to reduce shopping cart abandonment.
  • AI significantly improves website user experience by making it intuitive, accessible, and easy to navigate.
  • AI-powered social media tools can help you monitor and gain valuable insights about your brand. You can then use this to develop a social media marketing strategy to gain new customers.

Achieve these milestones, and you’ll be sure to acquire new customers and retain existing ones.

Feature Image Credit: iStock/monsitj

Sourced from https://www.blackenterprise.com

By Louis Columbus

Bottom Line: Understanding which pricing strategies cause buyers to progress through buying processes in a downturn still isn’t completely understood, but AI-based pricing can help remove blind spots in how pricing drives more sales during recessionary times.

The Struggle To Make Quota Is Real

Even in stable, healthy economic conditions, just 42% of sales professionals are making quota based on Salesforce’s State of Sales Report. Only 16% will be over 100% of quota in a given year. In an economic downturn, these numbers shrink, making the struggle very real to make quota in a recession. Here’s what it’s like to compete on pricing during a downturn:

  • Go-to pricing strategies that worked in better economic times fall flat and don’t generate 10% of what they before, with B2B-based selling teams seeing this most often.
  • Sales reps’ email in-boxes are either silent or filling up with requests for lower pricing, price protection, discounts, stock balancing or worse, returns.
  • Many CEOs, senior management teams, and sales reps’ initial goodwill calls to the top 20% of customers offering their complete support are now turning into returned calls asking for price breaks and permanent re-negotiated pricing.
  • Low-priced competitors surviving on single-digit margins continue their price wars, trying to keep production operating with orders while trimming staff.
  • Videoconferences are keeping deals alive in B2B pipelines, but when it comes to pricing, deals often stall as CFOs and their staffs review every new expense, introducing new members of the buying process at the last minute.

CROs say that sales cycles vary by industry, with automotive being the slowest and medical device manufacturing, medical plastics including PPE production, and consumer packaged goods manufacturers being the fastest. Getting pricing right has never been more critical or challenging, according to the CROs I’ve had conference calls with. When asked where AI is making a difference, several said automating special pricing requests, taking the drudgery out of managing co-op reimbursements, or researching sales prospects using automated services. Generating fully-priced quotes faster than competitors is where AI is most paying off according to the CROs I spoke with and is contributing to more won deals.

5 Ways AI Can Help Close More Deals In A Downturn

It’s counterintuitive to consider a downturn as a good time to find new ways to improve margins with more effective pricing. But that’s just what distributors, discrete and process manufacturing CROs are looking to accomplish today.  A 1% price increase can deliver a 22% increase in EBITDA margins and a 25% uplift in stock price according to McKinsey’s recent pricing research provided in the article, Pricing: Distributors’ most powerful value-creation lever. CROs are looking at when, how, and if they will increase prices on the most price-inelastic products they have. The logic behind prioritizing price-inelastic products is that selling on quality, availability, and build-to-order flexibility for customers buying these products is the goal to stabilize and grow margins. The smartest CROs I’ve met realize that engaging in price wars on price-inelastic products cost everyone margin, and no one wins. They’re also benchmarking their recovery efforts using EBITDA. The following graphic illustrates why pricing is so powerful, especially for distribution-centric businesses. Source: Pricing: Distributors’ most powerful value-creation lever. McKinsey & Company,  September 16, 2019

The following are five of the many ways AI can help close more deals in a downturn:

1.    Knowing why specific pricing strategies succeed or fail on a deal-by-deal basis in a downturn often defies easy explanation, which is why commercial analytics are so important now. Combining traditional win/loss deal analysis and AI-based commercial analytics provides new insights into what’s working in a downturn. Commercial Analytics suites that are the most effective combine transactional analysis with product and service mix, price, and volume analysis. Vendavo’s approach to providing commercial analytics is noteworthy for its streamlined, intuitive interface design that supports drag-and-drop report customization, real-time configurable alerts, integration to the price management module, pricing localization, and more. The following is an example of how Vendavo’s PricePoint works:

2.    By using AI’s supervised and unsupervised machine learning algorithms to improve risk scoring, only pursue opportunities that show the greatest margin growth and least downside risk.  CROs see the potential for AI to improve the cognitive functioning of sales, sales operations, and pricing working together to price and win the most profitable deals that have the least risk. The challenge is to take into account entirely new buying groups comprised of personas sales teams haven’t interacted with that much in the past. The pandemic and resulting downturn have completed changed group buying dynamics and introduced new risk factors into sales cycles not seen before. When the data is available, it’s possible to quantify the impact of risk factors on margin, price, and revenue gains.

3.    Help sales teams be more effective by improving Deal Price Guidance with AI, reducing the heavy cognitive load many are dealing with as pricing changes happen several times a week, along with new bundles and promotions. No one is talking about how sales teams are struggling to make sense of the many pricing, promotional, and bundling offers that are increasing today. Chances are your sales teams are overwhelmed with pricing reports, new pricing updates, promotional programs, rebates and bundles as many are. In good economic times, sales reps are sending, on average, 27%, nearly a third of their week, on internal administrative activities according to a recent Forrester/SiriusDecisions study.

4.    Tailoring up-sell and cross-sell recommendations for each customer using AI to define the optimal series of options and alternatives increases the average deal size and only presents the most buildable, profitable products to them. AI-based product recommendation engines integrated with CRM, e-Commerce, ERP, and pricing systems recommend the products and services that have the highest propensity of being purchased. The most advanced AI recommendation engines take into account previous buying behavior and buying patterns in making their recommendations.

5.    Knowing how price, volume, and mix decisions over time impact sales across product lines, sales teams, and business units is another area where AI is helping to improve sales in this downturn. It’s common to find groups of Sales Analysts crunching this data using Excel, which is a time-consuming, iterative process that’s a perfect candidate for AI-based automation. Imagine if the many Sales Analysts crunching data had more time to analyze it and see why pricing decisions by product, region, business unit, and geography are outperforming median sales and profit levels? Finding the reasons why pricing decisions are working in a downturn is how every CRO I’ve known defines a recovery plan. Vendavo’s recently-announced Margin Bridge Analyzer is an example of how AI can be used to understand better what’s hidden in the thousands of Excel spreadsheets organizations use for tracking pricing effectiveness.


Achieving commercial excellence in a down economy needs to start by improving pricing effectiveness that delivers solid gains to EBITDA margins over time. Of the many ways AI is improving selling, pricing, and margin performance, the five key areas helping distributors, discrete and process manufacturers the most are discussed in this post. McKinsey finds that the best short-term/high-impact an organization can make is concentrating on pricing and promotions shown in the graphic below. Improving sales in a downturn is possible when AI is used to decipher the data this recent downturn is producing and find new margin opportunities fast.

Source: Rapid Revenue Recovery: A road map for postCOVID-19 growth, McKinsey & Company, May 7, 2020

Feature Image Credit: ISTOCK

By Louis Columbus

I am currently serving as Principal, IQMS, part of Dassault Systèmes. Previous positions include product management at Ingram Cloud, product marketing at iBASEt, Plex

Sourced from Forbes

The London-based startup Auxuman plans to release a new AI-generated album a month, in a quest to see if robots can be creative geniuses.

What: AI musicians are a growing trend.

Who: Auxuman, an artificial intelligence startup up based in London

Why we care: Robots are coming for your playlist! AI personalities like Yona, Mony, Gemini, Haxe, and Zoya have the ability to put out a new full-length album via Auxuman every month. On average, most human musicians release one or two studio albums in a year, while rappers can put out up to three or four mixtapes in the same period, according to Digital Trends.

Auxuman dropped its debut album on September 27 and plans to continue releasing AI-generated albums every month on YouTube, SoundCloud, and elsewhere. The music is generated through engines that create the words, melodies, and a digital singing voice.

Does this mean that AI could be the death of human musicians? No. It’s not doomsday for the music industry just yet. The AI personalities sound like robots, which is certainly not everyone’s taste. There’s also the cult of personality. The average fan would probably prefer to imagine an actual human being behind the vocals and synths, no matter how autotuned they are. Nothing beats actually meeting idols in the flesh. However, AI could be another supplementary creative component used in music.

“There is always [a shortage of people] giving birth to a new genre,” Ash Koosha, Auxuman founder, told Digital Trends. “Due to the economic nature of the act of making music for humans, we are naturally submissive to forms that have been successful. We believe machines [can] blend and merge forms and styles [and in the process], find the next exciting sound.”

But then where does that leave the concept of creativity? The dictionary definition of creativity is the use of imagination or original ideas, especially with regard to artistic work. A computer can’t be imaginative; it’s interacting with algorithms and data. So the human mind wins this round, but who’s to say there isn’t a genius tune to be made by interweaving AI-generated sounds into notes arranged by human beings? So, musicians and producers, keep your instruments and Magix Music Maker locked and loaded.

Featured Image Credit: [Photos: Icons8 team/Unsplash; Franck V./Unsplash]

By Starr Rhett Rocque

Sourced from Fast Company

By Jessica Burton

Today’s cities are living entities. They develop, grow and become more complex over time. Yet, many of their most pressing issues, such as the need for utility improvements and monitoring crime, remain the same. Like never before, city officials have the capabilities to implement analytics technology. But surveillance will be at the heart of smart cities.

These technologies will help with a myriad of everyday city demands, in addition to more intricate challenges pertaining to security, healthcare, mobility, energy and economic development.

We need accurate insights into cities like never before.

With more than half of the world’s population residing in cities, this need for smarter and more accurate insights into their everyday workings is monumental. City management officials could learn much from leaders like Cisco, Amazon and Google. These companies have made it their business to not just collect data, but  utilize it to improve livelihoods and communities.  As we look to their successes, it becomes increasingly evident that the answer to creating smarter cities lies largely in surveillance technology that captures data analytics.

With the rise in surveillance technology and predictive analytics, we can make smart cities smarter and effectively, increase their efficiency. The reality is, however, that connectivity is never a guarantee. Therefore, necessary data must be present, regardless of connectedness, to ensure real-time decisions can be made. Satisfactory amounts of local storage must exist to position the most perceptive data nearest to the point of compute. This speaks to the increasing importance of the edge, as well as embedded storage.

Growth in real-time data is causing a shift in digital storage needs.

The growth of real-time data though edge analytics is causing a shift in the type of digital storage cities need. Fast, uncompromised access to data is becoming ever more critical. With a recent study, Data Age 2025: The Digitization of the World from Edge to Core, estimating that 175 zettabytes of data will be generated by 2025, there has never been a greater volume of insights at our fingertips and cities must step up to develop ways to use this data for good. In many ways, cities are already doing this – from intelligent street lights optimizing routes based on traffic patterns to reduce emergency response time by 20 to 30 percent, to advanced surveillance cameras with analytics deployed to enhance security operations, leading to a reduction in crime by 30 to 40 percent. However, we can do so much more.

To be a true smart city today, cities will need an “edge tier” approach to store, filter and manage data closer to the sensors. To gain deeper insights, the data is then stored and analyzed for longer periods of time in the edge domain as well as in the cloud or backend. Edge analytics that capture and collect data on network video recorders (NVRs) make it possible to act in real-time. With this technology, cities can find missing persons, notify residents of nearby emergencies and send out traffic congestion warnings.

Data insights will provide many wide-ranging benefits to cities.

The opportunities data analysis and data-driven urban improvement present are both hugely exciting and impossible to ignore. Behavioral analytics, thermal cameras and AI engines in edge devices like NVRs are just a sampling of the technologies that have given us the ability to remain constantly connected on a vast network. By horizontally interrelating individual systems, we can now develop insights into various mechanisms. This includes patterns in electricity, water, sanitation, transportation, environmental monitoring and weather intelligence.

West Hollywood’s Innovation Division is an excellent example to look to.

Take for instance, West Hollywood’s Innovation Division, which recently received the American Planning Association (APA) Technology Division’s Smart Cities Award for the “WeHo Smart City” Strategic Plan. Its three-part plan consisted of strategies including:

  • Data-driven decision-making rolling out to departments citywide
  • Collaboration and experimentation designed to enable City Hall staff to work better together.
  • Automation of processes to improve public safety and manage the built environment through smart city sensors and smart building programs.

With data collected from predictive analytics based on Deep Learning activities in the back-end, in some cases for over a year, we can pre-identify trends to manage incidents in one sector that directly impact another.

Access to real-time data and surveillance tech is key.

Cities need data in the moment and on the go. This places  a larger demand on the edge to produce the predictive and reliable information required, often in real-time. In fact, reports (Seagate) predict that due to the infusion of data into our city workflows and personal streams of life, nearly 30 percent of the “Global Datasphere” — meaning the amount of data created, captured or replicated across the globe – will be in real-time by 2025.

That’s a lot of real-time data. So, how can a city implement surveillance technology to better secure a city and enable smarter analyses? The first step is identifying video storage solutions positioned at the center of a smart city’s surveillance application. These solutions enable recordings, data retention, predictive analytics and real-time alerts. The next step is to position data at the edge and provide ample time for cities to make sense of patterns. More than ever before, cities will need to come together to integrate their technologies and ultimately make their networks smarter. This is a challenge that will require broad cooperation across its systems. Surveillance storage technology is the foundation to this strategy, ensuring timely data access and availability from edge to cloud.

By Jessica Burton

Global Product Marketing Manager at Seagate Technology. Jessica Burton has over 10 years of experience in IT storage and is the Global Product Marketing Manager at Seagate Technology. Her previous experience includes expertise in enterprise storage at Hewlett Packard Enterprise.

Sourced from readwrite

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


‘Summoning the demon.’ ‘The new tools of our oppression.’ ‘Children playing with a bomb.’ These are just a few ways the world’s top researchers and industry leaders have described the threat that artificial intelligence poses to mankind. Will AI enhance our lives or completely upend them?


There’s no way around it — artificial intelligence is changing human civilization, from how we work to how we travel to how we enforce laws. Science Technology breakthrough cool stuff programming robotic science technology software science technology cool stuff

As AI technology advances and seeps deeper into our daily lives, its potential to create dangerous situations is becoming more apparent. A Tesla Model 3 owner in California died while using the car’s Autopilot feature. In Arizona, a self-driving Uber vehicle hit and killed a pedestrian (though there was a driver behind the wheel). Science Technology breakthrough cool stuff programming robotic science technology software science technology cool stuff

Other instances have been more insidious. For example, when IBM’s Watson was tasked with helping physicians diagnose cancer patients, it gave numerous “unsafe and incorrect treatment recommendations.”

Some of the world’s top researchers and industry leaders believe these issues are just the tip of the iceberg. What if AI advances to the point where its creators can no longer control it? How might that redefine humanity’s place in the world? Science Technology breakthrough cool stuff programming robotic science technology software science technology cool stuff

Below, 50 experts weigh in on the threat that AI poses to the future of humanity, and what we can do to ensure that AI is an aid to the human race rather than a destructive force. Science Technology breakthrough cool stuff programming robotic science technology software science technology cool stuff

5. Nick Bilton

Science Technology breakthrough cool stuff programming robotic science technology software science technology cool stuff


Nick Bilton. (Om Malik)

Other experts fear the unintended results of AIs being given increasingly mission-critical tasks. Author and magazine journalist Nick Bilton worries that AI’s ruthless machine logic may inadvertently devise deadly “solutions” to genuinely urgent social problems:

“But the upheavals [of AI] can escalate quickly and become scarier and even cataclysmic. Imagine how a medical robot, originally programmed to rid cancer, could conclude that the best way to obliterate cancer is to exterminate humans who are genetically prone to the disease.”

Science Technology breakthrough cool stuff programming robotic science technology software science technology cool stuff

6. Nick Bostrom

Science Technology breakthrough cool stuff programming robotic science technology software science technology cool stuff


Science Technology breakthrough cool stuff programming robotic science technology software science technology cool stuff
Nick Bostrom at the Future of Humanity Institute. (Future of Humanity Institute)

Academic researcher and writer Nick Bostrom, author of Superintelligence: Paths, Dangers, Strategies, shares Stephen Hawking’s belief that AI could rapidly outpace humanity’s ability to control it:

“Before the prospect of an intelligence explosion, we humans are like small children playing with a bomb. Such is the mismatch between the power of our plaything and the immaturity of our conduct. Superintelligence is a challenge for which we are not ready now and will not be ready for a long time. We have little idea when the detonation will occur, though if we hold the device to our ear we can hear a faint ticking sound. For a child with an undetonated bomb in its hands, a sensible thing to do would be to put it down gently, quickly back out of the room, and contact the nearest adult. Yet what we have here is not one child but many, each with access to an independent trigger mechanism. The chances that we will all find the sense to put down the dangerous stuff seem almost negligible. Some little idiot is bound to press the ignite button just to see what happens.”

Science Technology breakthrough cool stuff programming robotic science technology software science technology cool stuff

8. Jayshree Pandya


Few applications of AI are as potentially dangerous as autonomous weapons systems. As DARPA and other defense agencies around the world explore how AI could shape the landscape of modern warfare, some experts are deeply concerned by the prospect of relinquishing control over devastating weaponry to neural networks. Science Technology breakthrough cool stuff programming robotic science technology software science technology cool stuff

Jayshree Pandya, founder and CEO of Risk Group LLC, is an expert in disruptive technologies, and she has warned of how AI-controlled weapons systems could pose an existential threat to world peace:

“Technological development has become a rat race. In the competition to lead the emerging technology race and the futuristic warfare battleground, artificial intelligence (AI) is rapidly becoming the center of global power play. As seen across many nations, the development in autonomous weapons systems (AWS) is progressing rapidly, and this increase in the weaponization of artificial intelligence seems to have become a highly destabilizing development. It brings complex security challenges for not only each nation’s decision makers but also for the future of the humanity.”

Science Technology breakthrough cool stuff programming robotic science technology software science technology cool stuff

9. Bonnie Docherty

Science Technology breakthrough cool stuff programming robotic science technology software science technology cool stuff


Some view the competition among software developers to create increasingly sophisticated AI as a contest eerily reminiscent of the Cold War era nuclear arms race.

Bonnie Docherty, associate director of Armed Conflict and Civilian Protection at the International Human Rights Clinic at Harvard Law School, believes that we must stop the development of weaponized AI before it’s too late:

“If this type of technology is not stopped now, it will lead to an arms race. If one state develops it, then another state will develop it. And machines that lack morality and mortally should not be given power to kill.”

Science Technology breakthrough cool stuff programming robotic science technology software science technology cool stuff

10. Max Erik Tegmark


Science Technology breakthrough cool stuff programming robotic science technology software science technology cool stuff
Technological advancements such as autonomous vehicles represent a paradigm shift in human society. According to Max Erik Tegmark, physicist and professor at the Massachusetts Institute of Technology, they also represent weaknesses that rogue actors will be able to exploit in future wars:

“The more automated society gets and the more powerful the attacking AI becomes, the more devastating cyberwarfare can be. If you can hack and crash your enemy’s self-driving cars, auto-piloted planes, nuclear reactors, industrial robots, communication systems, financial systems and power grids, then you can effectively crash his economy and cripple his defenses. If you can hack some of his weapons systems as well, even better.”



Sourced from boredpanda

The latest item to qualify in the “the Dystopian Future Is Now” category is a system that aims to help bartenders keep track of their customers with facial recognition and AI.

There’s just one teensy problem: Bartenders are really, really not into it.

The British company DataSparQ announced the launch of the “A.I. Bar” Thursday. It works by using cameras and sensors to place people who come up to the bar in a queue; it’s like a digital “take a number” system, but based on facial recognition.

People can see where they are by viewing themselves on a screen (with a number above their head, depicting their place in line). Bartenders will know who to serve by consulting a tablet that has pictures of all the people, in order of who they should serve first. It’s supposed to also help bartenders keep track of whose ID needs to be checked.

Recently, DataSparQ tested the system at a bar in London. Here’s a video showing how it works.

DataSparQ positions the facial recognition queue system as a win-win. It says customers will have less wait time, and that bars are poised to make more money with faster service. Customers, supposedly, hate waiting for drinks in line. But bartenders think the AI makes a problematic, technological mountain out of a decidedly human molehill.

“I just don’t see what this sort of technology would remotely do to mitigate all of the supposed service delay-based qualms that people have,” Asif Rizvi, a  Brooklyn bartender at The Breakers, said. “At best it’s unnecessary. At worst, it’s yet another harbinger of the impending AI apocalypse.”

Mashable spoke with four bartenders in New York, San Francisco, and Las Vegas about their impressions of the queue system. Here’s what they think about the prospect of gettin’ a little help from an AI friend.

“We know what’s going on”

As a 5-foot-1 woman, there have been times at busy bars when I’ve wondered, “does my bartender even know I’m here?” The short answer is, yes — which is why most of the bartenders I spoke with didn’t see the basic need for this sort of system at all.

“Really good bartenders are really good at their own facial recognition algorithm, which is called common sense,” Rob Ready, the co-owner of the SF bar Piano Fight, said.

Essentially, the problem that this is purporting to solve is that people have trouble getting served in a fair order, in a timely fashion.

“The unspoken presupposition for at least the way they frame it is that bartenders have no idea what they’re doing, and don’t know how to handle this stuff,” Rizvi said. “We know what’s going on. We see it all.”

If the value proposition of the product is to be believed, bartenders, supposedly, have a hard time keeping track of who arrived when, and who needs a drink. According to the bartenders, this is one of the challenges of being a bartender, but not an insurmountable one. In fact, being able to manage this is part of what makes a bartender good at their job.

“The best bartenders are very present, and know what’s going on in their surroundings,” Olivia Hu, the co-owner of Oldtimers in Bushwick, said.

The Breakers, where Rizvi works, is one of those multiple-people-deep, all-the-way-around-the-bar, hot Brooklyn weekend spots. He says that service is challenging, but all bartenders have their patterns and workflow that make them, ya know, qualified for their jobs.

“We’re not computers, obviously, and I guess that’s kind of the point here,” Rizvi said. “But we have a habitual checking of things in certain orders that ultimately will average to everyone getting dealt with evenly.”

In short: Calm down, the bartender will get to you.

“Maybe it could work in a non-tipping culture”

The person this system seems made for is, again, someone who feels like they have problems getting served. But there’s a simple solution to this problem, that doesn’t take money out of the pockets of your hard-working barkeep: tipping.

“It’s the people that don’t take care of the bartenders that tend to get forgotten at the bar,” Daniel Keaveney, a veteran Las Vegas casino bartender who is currently works at the downtown Las Vegas restaurant Esther’s Kitchen, said.

Keaveney stressed that discretion about who gets served is a huge part of a bartender’s job, especially in a place like Las Vegas. Automating the service line simply would not work in a place with a “tipping culture” where high rollers and good tippers expect service, pronto — and where bartenders rely on those tips, too.

Keaveney and the other bartenders acknowledged that tipping culture in Europe, where this product comes from, is very different.

“Maybe it could work in a non-tipping culture,” Keaveney said.

But in a place like Vegas, or even Bushwick, this system would change the way bartenders target tipping customers, especially loyal ones. It is possible, though, that supposedly equitable service orchestrated by AI could provide more tips from happy customers, but the bartenders I spoke to didn’t express that.

Plus, discretion in serving isn’t all about tips.

“It’s a very human interaction”

There’s a sacred relationship between a bartender and a customer, one that is not always necessarily financial. An AI just wouldn’t get that.

“I understand the nuances of serving alcohol,” Hu added. “It’s a very human interaction.”

Bartenders cultivate regulars by actually having conversations. Or bartenders can mete out drinks like justice, awarding the well-behaved, while teaching the assholes a lesson. It’s also up to bartenders to look out for the safety of their patrons. Ultimately, the bartenders believe that an AI system would get in the way of all of that.

“A big part of bars is social interaction,” Ready said. “If you come up to a bar, and you’re a dick to the bartender, then, yeah, it might take them a little bit longer to come serve you the next time around. And that’s good because it teaches people to not be dicks to service industry professionals.”

Bartenders also use a lot of personal judgment when deciding whether or not to hand an intoxicated person another drink. Maybe, eventually, an AI might be able to read that. But right now, a queue system might make cutting someone off way too complicated if they’re insisting on being served because their number is up.

“That is one of the major responsibilities of a bartender, to know when someone might be harming themselves with alcohol,” Hu said. “It’s difficult for a computer to make that call.”

“You’re trying to apply data and tech in a context that is very human,” Ready said. “That’s what doesn’t seem to fit.”

“I’d be creeped out”

DataSparQ said that it interviewed 2,000 people to learn that one of the reasons people leave bars is because they don’t like waiting in lines. But Ready thinks a wait in line is nothing compared to a machine capturing your biometric data and broadcasting it for everyone at the bar to see.

“I wonder if there was a question that said, if you saw yourself in a livestream video behind the bar, and the thing was scanning your face, and recognizing your emotions, where would that fall in the list of reasons to leave that bar?” Ready asked, um, hypothetically. “I’d be creeped out.”

Cities, countries, public, and private spaces are all grappling with whether and how to introduce facial recognition. These bartenders thought a watering hole was not a good place to start.

“There’s a privacy thing within a bar. You’re supposed to feel safe in a bar,” Keaveney said. “Maybe I don’t want to be seen in that bar like that, maybe I’m trying to hide out a little bit.”

To the bartenders, making a service system automated and transparent to customers doesn’t seem worth giving over your precious, biometric data.

“This doesn’t sound like anything more than a glorified take-a-number kind of system, and I don’t see why it has to collect your data in order to do that,” Rizvi said.

“You’re fixing something that’s not really broken”

Mostly, bartenders were flummoxed by how the system would actually help them.

“There’s never a situation where the bar is busy because there’s a ton of people there, and the solution is, ‘someone help these bartenders with facial recognition technology,'” Rizvi said.

First, they questioned how adding the extra step of consulting a tablet would actually make them able to serve more people, as the company claimed in its press release. Wouldn’t having to check a tablet, and match a face to a line item in a tablet, end up taking more time?

“They’re trying to solve a problem by creating a bigger problem,” Ready said.

Next, one of the problems the tech says it aims to solve is people pushing, shoving, and cutting in line. But the bartenders — keen observers of human nature — said that surely customers would find a way to game the system (say, by palling up with whoever’s first in line).

Finally, if someone really is bothered by waiting in line for a drink, they can always just … go to another bar.

“If you’re in a busy bar, there’s going to be a lot of people,” Rizvi said. “If you don’t like that, go to a different bar. There will always will be one.”

Mostly, the bartenders just didn’t see the need for the product in the first place. Yes, bars get busy. But it’s a bartender’s job to manage that, and a customer’s job to trust that person — and treat that person well — in order to keep the bar the functioning, very human place that it is.

“It just feels to me like it’s a fancy use of tech to solve problems that don’t exist,” Ready said.

Or, in Rizvi’s words, “you’re fixing something that’s not really broken.”

Feature Image Credit: Datasparq 

Sourced from Mashable

Sourced from AdAge

In the summer of 1956, 10 scientists and mathematicians gathered at New Hampshire’s Dartmouth College to brainstorm a new concept Assistant Professor John McCarthy called “artificial intelligence.” According to the original proposal for the research project, McCarthy—along with fellow organizers from Harvard, Bell Labs and IBM—wanted to explore the idea of programming machines to use language and solve problems for humans while improving over time.

It would be years before these lofty objectives were met, but the summer workshop is credited with launching the field of artificial intelligence (AI). Sixty years later, cognitive scientists, data analysts, UX designers and countless others are doing everything those pioneering scientists hoped for—and more. With deep learning, companies can make extraordinary progress in industries ranging from cybersecurity to marketing. It’s just a matter of knowing where to start.

Think of AI as a machine-powered version of mankind’s cognitive skills. These machines have the ability to interact with humans in a way that feels natural, and just like humans they can grasp complex concepts and extract insights from the information they’re given. Artificial intelligence can understand, learn, interpret, and reason. The difference is that AI can do all of these things faster and on a much bigger scale.

“In the era of big data, we have the need to mine all of that information, and humans can no longer do it alone,” says Mark Simpson, VP of offering management at IBM Watson Marketing. “AI has the capacity to create richer, more personalized digital experiences for consumers, and meet customers’ increasingly high brand expectations.”

The knowledge companies stand to gain by using AI seems to have no bounds. In healthcare, medical professionals are applying it to analyze patient data, explain lab results and support busy physicians. In the security industry, AI helps firms detect potential threats like malicious software in real time. Marketers, meanwhile, can use AI to synthesize data and identify key audience and performance insights, thus freeing them up to be more strategic and creative with their campaigns.

There’s something else AI is very good at, and that’s improving the relationship between companies and consumers. “Even in its earliest iteration, AI helped companies better understand how to be human,” says Brian Solis, author and principal analyst at Altimeter, the digital analyst group at brand and marketing consultancy Prophet. “The irony is that it took this very advanced technology to make them think differently about how they should communicate with their customers.”

Over the past 50 years, Solis says, advances like speech technology, automated attendants, virtual assistants and websites have opened a chasm between companies and customer engagement while also multiplying consumer touchpoints. But AI has the potential to close that gap.

By helping marketers collect data, identify new customer segments and create a more unified marketing and analytics system, AI can scale customer personalization and precision in ways that didn’t exist before. Connecting customer data from sources like websites and social media enables companies to craft marketing messages that are more relevant to consumers’ current needs. AI can deliver an ad experience that is more personalized for each user, shapes the customer journey, influences purchasing decisions and builds brand loyalty.

IBM’s Watson Marketing is leading the charge with a platform that capitalizes on all that AI has to offer. Products like Customer Experience Analytics lets marketers visualize the customer journey and identify areas where consumers might be experiencing friction. Companies get a more complete view of the customer journey, which they can then optimize to improve customer engagement and conversion rates. Since it’s delivered through a single, unified interface, IBM Watson Customer Experience Analytics makes gaining actionable intelligence a seamless process for brands.

According to market research firm TechNavio, the AI market in the U.S. is expected to grow at a compound actual growth rate of about 50 percent through 2021. In its 2017 report “Artificial Intelligence: The Next Digital Frontier?” the McKinsey Global Institute urges companies not to delay “advancing their digital journeys”—especially when it comes to leveraging AI. “It’s those who understand how to use AI in new ways, to create new mindsets and paradigms, that will instill a competitive advantage that wasn’t there before,” Solis says.

We’ve entered the age of deep learning, and with human guidance AI is finally reaching its true potential. Today, the technology McCarthy and his colleagues dreamed about in 1956 takes the form of AI platforms like Watson Marketing. And now is the right time to truly harness the power of AI and put it to work for business success.

Find out more about how Watson Marketing can uncover insights to help you better understand your customers. Read the Guide.

Sourced from AdAge

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