All Posts by Mike Oitzman

Man with a checklist

What is GS1 Verified? – Everything you need to know

​Live from the GS1 Connect 2019 Event

With the GS1 Connect 2019 event happening this week in Denver, Colorado, we think that it’s important to review the core principles of GS1 and how IceCream Labs is contributing to the success of GS1 and the companies who leverage GS1 standards in their business. This article will recap the show and introduce the technology that IceCream Labs is announcing to support GS1 standards.

Making the Pitch in a New Venue

IceCream Labs is competing in the startup pitch competition this week at GS1 Connect 2019​. We have a booth on Startup Row at the event and are thankful for the invitation to compete as one of eight startups participating in the GS1 Startup Lab and Pitch competition. The GS1 Connect event is one of the first events to take place in the brand new Gaylord Rockies Convention Center, and the event is bigger this year than ever before.

​Our CEO, Madhu Konety was live on stage with the other Startup Lab CEO's competing in the startup pitch competition.

GS1 Connect Startup Lab Participants

Image Courtesy of GS1 Connect Startup Lab

Quality Data is the Core of the Verified by GS1 Program

Accurate product data is key for every step of the supply chain process. Consumers expect that the information which they see on a product description page accurately describes the product they are about to purchase. Since an online consumer can’t touch the physical product that they are considering, the online product description page must present all of the information that the consumer needs to make their decision. This data includes all of the images, video and text which describe the product.

One side effect of online retailing that we’ve observed is “Attribute Creep” and growth in the total number and variety of product attributes displayed for consumers. Product images and videos also complicate the process of sharing information along the supply chain. Media files have historically been difficult for merchandising teams to version and track. IceCream Labs technology enables the merchandising team to verify the content in a product image.

All along the supply chain process, there are a variety of expectations which define the product attributes necessary for a given transaction. Any incorrect or incomplete piece of information can add cost to the entire transaction. Merchandising and purchasing teams at a retailer need accurate data to buy from a supplier or manufacturer. Logistics managers need accurate product data to plan and move cargo. But it all starts at the manufacturer, who is creating and launching a new product for sale. Failure to create and deliver accurate product data at this point, will destroy trust through all subsequent supply chain interactions.

This is why ecommerce executives are starting to look closely at the product information process and putting the necessary steps in place to ensure that each transaction maintains the highest level of data quality.

GS1 Verified Image

Image courtesy of GS1 US

The Creation of a Minimum Product Data Set

While there can be hundreds of different product attributes necessary to move an item from the manufacturer to a consumer, there is a small set of attributes which are required at every step of the process. GS1 has worked diligently to distill this long attribute list down to the following 7 core attributes:

Unique Product Identifier (Global Trade Item Number or GTIN)

  • Brand Name
  • Product Description
  • Product Image URL
  • Global Product Classification
  • Net Content & Unit of Measure
  • Target Market

It’s these 7 attributes which make up the basis for the the Verified by GS1 program​. GS1’s role in the process is to help create and manage barcode data for product packaging, along with managing the GTIN registry. This ensures that each new product gets a unique product number which isn’t replicated anywhere else.

Managing the Product Data Creation Process

This is where IceCream Labs comes in. With artificial intelligence, IceCream Labs is able to automatically extract product content from product images with the IceCream Labs Catalog Management solution.

Next, the extracted product content can be compared to existing product data to ensure that the product packaging images match the existing digital product data. In this process, the existing product content and the extracted product content can be verified with the official product GTIN and then normalized to ensure that all of the data (images and text) is consistent. This validates that the images match the rest of the content (item 4 in the core GS1 attribute list above).

Enriched Product Content for Consistency

Unlike a rules based engine, AI is able to intuitively process a variety of product content and selectively enrich content differently, based on the market and usage of the content. The output is a verified and normalized product data set that is ready for publication in your production product catalog. This process helps to augment the tasks of a brand manager at the manufacturer or the merchandising team at a retailer.

IceCream Labs is one of the first technology companies to apply artificial intelligence to this problem and we are helping ensure that product data is consistent across all channels and types of content. The end result is the presentation of the best information to the consumer so that they can make the right choice.

Handheld phone with image of products

DataPorts and Why they Matter

Improve the Quality of Product Content

​Retailers today struggle with managing ​the product content necessary to publish and maintain their online product catalog. Assembling content from manufactures is difficult when information ​comes in different forms. Likewise, manufacturers struggle to publish new product content and syndicate content throughout their channel.


Standardization is one approach and it is important for critical attributes such as the global trade identification number (GTIN). However, information such as schemas remain difficult to implement throughout the value chain and across the various different markets and regions.

Recently, IceCream Labs become involved with the Consumer Goods Forum (CGF). One core goal of the CGF is to improve data exchange throughout the value chain. The momentum within the CGF comes from the attention of executive leadership from many of the largest worldwide retailers and manufacturers. As a result, the CGF is starting to see momentum build from its initiatives.

Make it Simple

The executives within the CGF want to achieve the goal of reducing the pain of sharing information and improving interactivity (while reducing the costs of managing this data). From this need has emerged the idea of DataPorts. Conceptually, DataPorts deliver a method for peer-to-peer data exchange between any two points in the value chain. This removes the need for data aggregation or hub and spoke interactions. Any point in the supply chain can talk to any other point.

At the heart of the DataPort implementation is the use of Graph Query Language (GraphQL) and GraphQL schemas. The significance is that the GraphQL is emerging as a performant solution to the need for quickly finding related information in a network of related data. GraphQL has evolved to meet the needs of social media giants Facebook and LinkedIn.

What is a DataPort?

At its simplest implementation, a DataPort is a method for publishing information and then discovering and using the information using data virtualization rather than data federation. There is a common programming model for DataPorts, and this allows for peer-to-peer integration.

For example, a manufacturer can release a new product line, publish the content to a DataPort and then make that DataPort available for any retailer to query (with their product catalog DataPort). A retailer can request the new product content, and specify the schema that they desire and normalize any values in the process.

The services delivered by a DataPort ​are broken down (currently) into three broad areas:

1. Abstraction is the process of virtualizing the source data.
2. Transformation operates on the data to do things such as unit conversion.
3. Composition takes a set of results and creates a response from the DataPort.

At IceCream Labs, we are actively working on applying our existing expertise and experience in  data extraction to use machine learning models in the abstraction and transformation processes. We are already normalizing ​and extracting data from source images and unstructured information to generate high-quality product content.

Stay tuned, as we continue to explore more ways that ​DataPorts are changing the way that data moves through the supply chain, and improve the entire end-to-end process.


Dataport Whitepaper Cover

​Download your copy of the latest DataPorts whitepaper: Solving End-to-End Value Chain Content Integration from the Consumer Goods Forum

Tree Branches depicting product categorisation

How can I use AI to Categorize Product Data

Is there a better way to leverage AI to categorize product data?

Have you ever tried searching for a product on your favorite online shopping site, only to be disappointed when you couldn’t find the product that you’re looking for? Most product site search engines leverage accurate product categorization attributes to help narrow the search results for a user.

In this article we’re going to look at the impact that proper categorization has on search and how it’s now possible to automate product categorization with a machine learning model.

What is Categorization?

Categorization starts with a well designed product category taxonomy. The product taxonomy defines how each product type is related. The first couple levels of a product taxonomy contain broad category labels. For a grocery taxonomy, the top levels might be organized by departments within the store. It’s a logical representation of the way that a shopper would look for a given product in the physical store. A taxonomy is often referred to as a “Product tree”, with each product category referred to as a “branch” and each individual item referred to as a “leaf” on that branch.

Grocery taxonomy example:

  1. Meat & Seafood

    1. Fresh Meat

      1. Ribs

      2. Smoked Ham

      3. Specialty Meat

      4. Kosher Meat

      5. ...

    2. Fresh Seafood

    3. Packaged Meat

    4. Packaged Seafood

  2. Produce

  3. Deli

  4. Bakery

  5. Adult Beverages

  6. Beverages

  7. Floral

  8. ...

For a new product to be put into the online product catalog, it first needs to be categorized appropriately into the correct level of the product taxonomy. This is easy enough for a human to complete the product categorization, however, when you have thousands and thousands of products, this can be a tedious process.

Why is Categorization Important?

The science of search has evolved over the last two decades. Trying to determine the searchers intent from one or two words is not a simple process. We’re not going to dive into that in this article. However, in the specific use case of product search for an ecommerce website, most shoppers will generally include the object of their intent as part of the search input. In most cases this data can be used to quickly narrow the results set based on the product taxonomy. After all, the consumer isn’t looking for organic lettuce in the seafood section, nor would they be looking for seafood in the produce section. So one method to quickly close the search breadth is to narrow the search to specific sub-branch of the product taxonomy.

One downside to improper categorization is that improperly categorized products can become “lost”. When a product is mis-categorized on an improper branch of the taxonomy, the search engine may either (1) not find the product or (2) relegate the mis-categorized product to the bottom of the search results.

Don’t believe me? Try this: go to your favorite ecommerce provider, search for something, and then go to the last page of the search results. What do find there? Don’t let this happen to your product catalog.

In addition, the product category for a given catalog item can help define the product schema that should be employed to display the product information for the consumer on the product data page. The schema can also help define the meaning of generic product attributes, depending on the product type.

What is ATOM?

ATOM is the product categorization service from IceCream Labs. We developed ATOM as an API service which can be accessed automatically from your product information manager. ATOM takes a product title or description as an input and outputs the recommended product category for the item. ATOM is powered by a machine learning model that has been trained on millions of product records. It’s constantly learning as it processes new data.

With ATOM, you can properly categorize or validate a new product item before accepting it into your production product catalog.

To learn more about ATOM, or see a demo, contact our sales team: sales@icecreamlabs.com


A Tour of CatalogIQ for Grocery

Kick-start eCommerce sales with awesome product data

E-commerce requires awesome product data to support successful search and conversion. Product data for the online grocery market is currently being created manually. Retailers are struggling to acquire the rich product data necessary to support their online needs. Brands are struggling to generate good data.

ContentIQ Add Product

IceCream Labs CatalogIQ is designed to automatically extract attributes from product images. Using either label mechanicals or actual product images of the packaging, CatalogIQ can extract text from the labels. From there, the artificial intelligence in CatalogIQ understands what the text is and inserts it into the appropriate product attribute. The AI can also determine which images are the hero image, and front, back and side images.

CatalogIQ Extracted Attributes

CatalogIQ identifies brand names, sub brands and variants, normalizing the brand to the appropriate text. titles are generated from various attributes to create an SEO-rich title to optimize search. Other key attributes include feature/benefits, ingredients and nutrition facts.

CatalogIQ extracting content from a product

Sample CatalogIQ extraction (front/rear)

How complete is your data?

ContentIQ Catalog List view

CatalogIQ can score the data to help the merchandising and ecommerce teams understand which product records have been enhanced.

  • Missing attributes
  • Accuracy of attributes (are all of the attributes congruent?)
  • How unique are the attributes?
  • Is your product record SEO optimized?
  • Do you have relevant search keywords?
  • How well does your product data match up to customer site searches?
catalog IQ demo screen

Support for Grocery merchandising teams

Grocery merchandising teams have the chore of uploading new catalogs from suppliers and manufacturers. Often this data arrives in the form of a spreadsheet. CatalogIQ can easily upload a new catalog file (in spreadsheet form) to quickly and easily complete the ingestion process.

ContentIQ Add Catalog screen

Support for Grocery and CPG Brands

Grocery retailer and channel partners expect high quality product data to list and sell your products online. Can you deliver the content?

CatalogIQ allows brand and product managers to auto-generate high quality product data directly from product label mechanicals and/or product images. If you're currently using manual processes to create product content and to check the accuracy of product data, then let CatalogIQ help you automate the creation process. You'll be able to complete the data creation process much faster than manual methods. CatalogIQ can also validate the content and ensure that it matches what is contained on all of the product labeling.

CatalogIQ Features

  • Quality product images including relevant Nutrition Facts
  • Accurate meta-data, including attributes like: allergens, sugar free, Kosher certified, Non-GMO and other facets
  • Complete, standardized and SEO enabled titles
  • SEO rich descriptions
  • Correct product categorization

As a merchandising manager with a large product catalog, you know the difficulties of reviewing your product data and ensuring that everything in the catalog is ready to publish live to customers. There is always the nagging concern that something is inaccurate or missing when you push the “publish” button. Every time that you receive new data from your suppliers, it’s a chore to process the data. You have a long checklist to complete before you can publish data to the live catalog. Processing this checklist can consume all of your time.

CatalogIQ Benefits

  • High quality product data
  • Improve product page discoverability 
  • Increase product sales
Innovation Lab award

ICECREAM LABS WINS INNOVATION AWARD AT SHOP.ORG TRADESHOW

FOR IMMEDIATE RELEASE:

ICECREAM LABS WINS INNOVATION AWARD AT SHOP.ORG TRADESHOW

SHOP.ORG recognizes top innovation technology providers at the 2018 SHOP.ORG tradeshow

LAS VEGAS, Nevada – Sept 14, 2018 – IceCream Labs, the leader in intelligent merchandising solutions for e-commerce providers, was awarded an inaugural Innovation Award at the 2018 SHOP.ORG event for the “Best Awareness Tech”. IceCream Labs was one of six exhibitors to be recognized with an innovation award.

“It is exciting to celebrate these pioneering companies that are leveraging technology and digital innovation to reimagine the retail experience,” NRF President and CEO Matthew Shay said. “These recipients represent the true spirit of innovation. The extraordinary new products, services and experiences they have created are poised to play a transformative role in the retail industry.”

The winners were chosen from among the 34 exhibitors at the conference by a panel of judges representing venture capitalists, technology incubators, retailers and journalists. Companies had to showcase their ability to most effectively apply the latest advances in artificial intelligence, augmented reality, machine learning, facial recognition, data analytics, robotics and more. The winners were selected based on the impact of their innovation, product execution, ability to scale and market fit. Of the six winners, IceCream Labs, Allure Systems, Hero, and ZigZag were chosen among categories at the conference’s Innovation Lab and Mystore-E from the Startup Zone while Trade’EM was designated as the “Shop.org Attendee Choice Award” selected by attendees voting via the conference’s mobile app.

The winners will receive spots in the Innovation Lab or Startup Zone and participate in a session on the Innovation Lab stage next year at NRF 2019: Retail’s Big Show. NRF’s annual convention is scheduled to be held January 13-15, 2019, in New York as over 37,000 retailers from nearly 100 countries gather to address the industry’s latest trends, innovations and strategies for transformation.

AI-based retail solutions from IceCream Labs help to improve retail customer experience

At SHOP.ORG, IceCream Labs demonstrated both the Intelligent Data Mesh (IDM) and the new IceCream Labs StyleIQ™ solution. StyleIQ is built on top of the IDM and leverages the artificial intelligence within the IDM to connect all of the products in the product catalog. StyleIQ helps merchandising and product managers automate their daily workflow. StyleIQ leverages the power of the IDM to understand all of the products within a product line and then illustrate unseen product groupings and relationships within the catalog. StyleIQ can then compare the existing product line to what’s trending on social media. By leveraging the power of AI to autonomously make the product associations, StyleIQ can generate new product collections, ensembles and “looks”.  In the online grocery market, StyleIQ can be applied to help automatically fill a shopper’s cart with products related to a given recipe, cuisine or dietary limitation.

For an e-commerce or merchandising manager, the payback from the IDM and StyleIQ comes in multiple forms. First, StyleIQ helps automate the daily workflow tasks of the merchandising manager to reduce their workload. Working as a virtual assistant, StyleIQ can research the latest social media trends and suggest new product configurations. Second, the IDM can automatically enhance and enrich your product catalog data directly from your PIM or MDM. You can still use your existing PIM for all of your product data governance processes, knowing that it is being enriched with complete data. Finally, by providing online customers with an enhanced shopping experience, you improve their ability to visualize the choices in your product line, increase the “discoverability” of your products online and improve the opportunity for both conversion and an increased gross merchandise volume at checkout.

The Intelligent Data Mesh and CatalogIQ are available now at www.icecreamlabs.com. StyleIQ is in the demonstration phase and customers can schedule a live demo through the IceCream Labs website.

About IceCream Labs

IceCream Labs is the only AI powered platform which provides on-demand Intelligent Merchandising solutions for e-commerce retailers, brands and marketplace sellers. We help you realize the maximum potential of your product catalog by boosting the quality of your product content to create an immediate impact on revenues and operations.

Several of the world’s largest retailers have benefited from our Intelligent Merchandising platform. Deep learning applications on the platform have delivered results with absolute precision, accelerating revenues up to 4X for our customers.

Our new catalog data quality platform consumes product data coming in through various sources. Applications on the platform continuously process and profile the content quality of over 100 million products and 50 million images empowered by our big data algorithms. This data is interpreted by our multiple patent pending Deep Learning models to cleanse, enrich and optimize your product content. The output of the models can be integrated seamlessly with your existing solutions to help you reach your business goals.

At IceCream Labs, we believe in the value of technology and its ability to disrupt traditional business models. Our Culture – like technology – is open and without any boundaries. We believe in the power of providing simplicity while managing the complexity behind it, by keeping our focus constantly on Innovation and Execution.

CONTACT:

Mike Oitzman

IceCream Labs

mike.oitzman@icecreamlabs.com

Web: icecreamlabs.com

Ph: 530-270-9466

###

ICECREAM LABS DEMONSTRATES INTELLIGENT DATA MESH AT SHOP.ORG TRADESHOW

FOR IMMEDIATE RELEASE:

ICECREAM LABS DEMONSTRATES INTELLIGENT DATA MESH AT SHOP.ORG TRADESHOW

The Intelligent Data Mesh uses machine learning to extract features, enrich product attributes and extend your product catalog, resulting in a better shopping experience for customers

LAS VEGAS, Nevada. – Sept 13, 2018 – IceCream Labs, the leader in intelligent merchandising solutions for e-commerce providers, today unveiled the Intelligent Data Mesh (IDM), an AI-based platform that leverages machine learning for unparalleled catalog data quality management. The IDM enables companies to maximize the potential of their product catalog, resulting in immediate impacts to revenues and operations.

“Merchandising managers have a tough job today to stay up on the latest trends in their markets. Selling online has changed many of the classic rules of merchandising and as a result, it can be tough to stay on top of what consumers desire”, said IceCream Labs founder and CEO Madhu Konety. “It can be exhausting to stay on top of the latest social trends and react quickly in your product line to those trends. We have solved this problem for the merchandising manager by leveraging AI to connect all of the dots, while providing suggestions on how to assemble new ‘looks’ in their product lines; all based on social data signals.”

Introducing the Intelligent Data Mesh and IceCream Labs StyleIQ™

The Intelligent Data Mesh (IDM) is the core platform for all of the IceCream Labs solutions. At the center of the IDM sits the machine learning models which are leveraged to extract and process product data from any content source. The IDM’s artificial intelligence engine enriches product content by generating missing attributes, meta-data and product titles. The outputs of these operations are made available through the IceCream Labs CatalogIQ™ solution. CatalogIQ is especially beneficial for product catalogs with a large number of products.

 “The Intelligent Data Mesh commercializes our machine learning ML models, making them applicable to multiple market segments,” added Konety. “We now have ML models capable of processing image and source data from the grocery, home and garden, and fashion markets. We have also generalized the generation capabilities of IDM to make it functional for any market segment. The IDM can directly pull and push data into/out of a PIM, MDM or CSP.”

At Shop.org, IceCream Labs is also demonstrating the new IceCream Labs StyleIQ™ solution. StyleIQ is built on top of the IDM and leverages the AI within the IDM to connect all of the products in the product catalog. StyleIQ helps merchandising and product managers automate their daily workflow. StyleIQ leverages the power of the IDM to understand all of the products within a product line and then illustrate unseen product groupings and relationships within the catalog. StyleIQ can then compare the existing product line to what’s trending on social media. By leveraging the power of AI to autonomously make the product associations, StyleIQ can generate new product collections, ensembles and “looks”.  In the online grocery market, StyleIQ can be applied to help automatically fill a shopper’s cart with products related to a given recipe or cuisine.

For an e-commerce or merchandising manager, the payback from the IDM and StyleIQ comes in multiple forms. First, StyleIQ helps automate the daily workflow tasks of the merchandising manager to reduce their workload. Working as a virtual assistant, StyleIQ can research the latest social media trends and suggest new product configurations. Second, the IDM can automatically enhance and enrich your product catalog data directly from your PIM or MDM. You can still use your existing PIM for all of your product data governance processes, knowing that it is being enriched with complete data. Finally, by providing online customers with an enhanced shopping experience, you improve their ability to visualize the choices in your product line, increase the “discoverability” of your products online and improve the opportunity for both conversion and an increased gross merchandise volume at checkout.

The Intelligent Data Mesh is available now at www.icecreamlabs.com. StyleIQ is in the demonstration phase and you can see a demonstration at the IceCream Labs booth (#IL4) in the Innovation Lab at Shop.org this week.

About IceCream Labs

IceCream Labs is the only AI powered platform which provides on-demand Intelligent Merchandising solutions for e-commerce retailers, brands and marketplace sellers. We help you realize the maximum potential of your product catalog by boosting the quality of your product content to create an immediate impact on revenues and operations.

Several of the world’s largest retailers have benefited from our Intelligent Merchandising platform. Deep learning applications on the platform have delivered results with absolute precision, accelerating revenues 4X for our customers.

Our new catalog data quality platform consumes product data coming in through various sources. Applications on the platform continuously process and profile the content quality of over 100 million products and 50 million images empowered by our big data algorithms. This data is interpreted by our multiple patent pending Deep Learning models to cleanse, enrich and optimize your product content. The output of the models can be integrated seamlessly with your existing solutions to help you reach your business goals.

At IceCream Labs, we believe in the value of technology and its ability to disrupt traditional business models. Our Culture – like technology – is open and without any boundaries. We believe in the power of providing simplicity while managing the complexity behind it, by keeping our focus constantly on Innovation and Execution.

CONTACT:

Mike Oitzman

IceCream Labs

mike.oitzman@icecreamlabs.com

Web: icecreamlabs.com

Ph: 530-270-9466

###

shopping cart in a grocery store

Blockchain — The Retail Advantage

Blockchain — The Retail Advantage

The retail landscape has changed over the last decade. With newer technological enhancements, more retailers are opting to incorporate the latest technology to stay ahead of the competition.

From drone deliveries to one hour deliveries, companies are increasingly investing in virtual assistants and AI to engage with customers and enhancing customer experience.

Blockchain is not far behind. With its satisfying results by effectively creating a complete virtual financial market, it is here to stay. It’s no surprise that blockchain is revolutionary technology. The retail industry has finally recognised the power blockchain holds.

Here’s how blockchain can play a crucial role in the future of retail:

Data Collection and Analysis

Data enhances the shopping experience for customers. Blockchain does that, by delivering an efficient way to collect and analyse the available information. Leveraging AI, Blockchain can gather and assess data in real time from different sources like consumers and retailers.

There is a lot of data now that is available from different locations. Unfortunately, the data that is available is fragmented. This makes it difficult to sift through and detect patterns that can let the retailer know which direction they have to take to enhance their customers’ experience and address their pain points. These become missed opportunities for retailers. Here, the blockchain technology can aptly address this problem and make it easier. A blockchain platform collects data from across the supply chain and leverages machine learning to structure the data. Blockchain can enhance the inventory tracking process, including reducing overstocking and under-stocking. Since blockchain uses a secure ledger format, the product data is more reliable and secure from tampering. Furthermore, it can reduce supply chain product waste.

blockchain-technology

Supply Chain Ledger

There are several ways that blockchain can help strengthen relationships. The entire supply chain is one major aspect retailers need to keep secure as it directly impacts the shopping experience for the customers. Using blockchain as a supply chain ledger can make a huge difference in all segments of retail. This especially can impact and enhance retailers that are involved in perishable goods.

Blockchain ensures that the supply chain and logistics is secure and authentic. This means that every record and form is being checked and time stamped in the supply chain. This ensures no tampering of data with everything being independently verified. Thus, there is greater control over product manufacturing location, process, and timing.

Furthermore, a blockchain supply chain model also enables retailers to control all aspects of transportation, storage, delivery, and presentation.

Payments and e-commerce

Blockchain is a trusted means of payments. The majority of retailers are integrating bitcoin and other cryptocurrencies as means of payment processing. The big advantage here is, compared to credit cards, the integration of cryptocurrencies is that it is cheaper and transparent to process transactions.

Blockchain allows for retailers to accept cryptocurrencies along with digital records which helps streamline refunds and return processes.

Besides, purchasing items that need a large amount of money such as cars or land property with cryptocurrency can track the ownership and verify resale of stolen goods.

e-commerce

Retailers must now realise that Blockchain is here to stay. While there are other developments happening in the industry, they need to keep an eye out for this technology.

The possibilities have only just begun.

Related AI Articles:

Roadsign pointing to AI / ML bandwagon

How to tell if a software vendor is really using AI

Is it possible to tell truth from marketing spin?

Artificial Intelligence (AI) has become an industry buzzword. It’s at the top of the hype cycle right now. As a result, sales and marketing people are using “AI” in sales pitches all the time. You see “AI” everywhere in the ad copy for all kinds of software solutions. If you’ve been to any trade show recently, then you've been blasted with "AI" messaging along every aisle of the show floor.

Looking for an AI-based software solution? Learn how to read through the AI marketing hype and determine if a software vendor truly has an AI-based solution. #AI #artificialintelligence #hypecycle

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VC’s are smart enough to recognize the difference, but are you?

VC’s have seen the trend coming for at least two years. Last year, at an investor conference that I attended, one of the VC’s on stage said that AI has become so trendy that the running joke between the partners was that they would immediately fund any company who pitched them a solution without mentioning AI in the pitch.

In a typical solution implementation, these “artificial intelligence” companies are actually just doing basic data analysis using classic software programming techniques. Bottom line: making data contextually relevant is not AI. If a software company is not using AI as a core technology, then their solution will never get smarter over time nor will it reach any level of autonomy.

Stop with all the marketing hype

Many software companies today are incorrectly using AI as catchall phrase for anything that has to do with data or workflow or robotics. However, at the center of any “True AI” company there has to be a data product: some data offering which is the output of the autonomous processing of vast amounts of information. Automating workflow or automated processing of data by itself does not constitute artificial intelligence.

Get smart with your vendors

Here are seven things to consider about your software vendor to determine if they are really employing AI in their solution or if they are just jumping on the AI marketing bandwagon.

  • Check their job board. The job market for AI and machine learning engineers is extremely competitive right now. It’s so competitive in fact, that most software vendors who are leveraging AI within their solution need to be on the constant lookout for skilled engineers. Look for engineering job openings with titles like: “machine learning engineer”, “data scientist”, “artificial intelligence”, “data science” and “big data”.
  • Who are their founders? Do the founders have a deep technical understanding of machine learning models and do they understand the need to apply it to large data problems? This isn’t to imply that a large enterprise software vendor who’s embarking on a new generation of AI-based solutions, has to fire their CEO and replace them with an AI-literate individual. However, most AI startups today are the brainchild of deeply AI-literate founders and it will be easy to determine if AI is a core part of their solution stack.
  • Are they targeting enterprise applications or SMB? This is a generalization, but AI-based solutions may not work adequately for small companies, unless the problem is defined by a larger data set that can be used to train a model. That model can then be used to process the smaller organization's data set. A good example here is Google or Facebook using the image data of everyone on the platform to be able to identify faces in your single image.
  • Can you use your own data in a demo or pilot? Any AI-based solution should be able to process your data during a demo or a pilot. It will be in your best interest during the demo to provide the largest data set possible as the vendor may need to use some of the data for training; some of the data for testing; and some of the data for demo’ing the output. Note: you should be clear about where any how and proprietary demo is consumed during a demo or pilot.
  • What data sources have they used to train the system? If they say that they don’t need training data, then they likely aren’t using AI or machine learning. If, however, they are able to explain the process by which they’ve trained their model and can point you to the source of their training data, then this is a good sign that they are actually using AI.
  • Can they provide detail on the algorithm used to process the data? Companies who use recurrent neural networks (RNN) to process their models don’t have any trade secrets. The use and implementation of RNN’s is an industry standard and the company should be able to explain how they’ve implemented an RNN into the workflow that you are interested in automating. The secret sauce, however, is in the combination of training data that the company has employed (or will employ) and how skillful they are at deriving a viable and repeatable model with a minimum of bias.
  • Do they have reference customers? If so, then talk to their reference customers about the reality of their AI claims.


Summary

The AI hype won't end anytime soon. The best that you can do for now is to educate yourself and be prepared to ask the tough questions during any vendor evaluation process. Good Luck!

Related AI Articles:

Artificial Intelligence

Top 10 Uses for AI in 2018

Here are the Top 10 Uses for AI in 2018

Introduction

More and more, artificial intelligence (AI) is being employed to help automate all aspects of your digital life. Many of the use cases are obviously AI-driven applications, but there are also other, less obvious solutions. In this article we’ll take a look at the top ten commercial applications areas for AI in 2018.

The Top 10 AI applications of 2018 illustrate just how far #AI has evolved in the last five years. @icecreamlabs

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1. Voice Search & Virtual Assistants

The past year has seen the launch of multiple consumer interfaces and devices which employ an interactive voice interface. From the Siri interface on Apple iPhones to the Alexa and Google Home devices, the application of voice search has evolved to become a real part of our lives. Unlike text search, which uses keyboard input to interpret search intent, the voice search engines require the interpretation of spoken word to understand what a user is asking. This technology has been evolving for decades, but it’s the maturity of machine learning and the compute power of the cloud which has enabled these solutions to become a reality today. The accuracy of voice search is still evolving and many consumers find more entertainment value than commercial value in their interaction with these devices.

The growth of Siri, Alexa and the other voice interfaces is breaking down the barriers for adoption and acceptance of AI-enabled solutions. This is the reason that we decided to put voice search at the top of our 2018 list.

Virtual Assistant Lineup


2. Ridesharing

Ridesharing solutions such as Uber and Lyft are growing in popularity as consumers find them a useful way to get around. The AI-enabled decision engines in these ridesharing tools look at both current demand and historical usage trends to help predict when and where consumers are going to request a ride. The result is that the tools can adjust pricing in what they call “Surge Pricing Models” to help attract drivers during peak periods and to provide a competitive alternative to other transportation choices. The effect is that Uber, for example, has created a transportation marketplace with a bunch of suppliers (e.g. drivers) competing to pickup customers. In the process, Uber is optimizing the fees paid by consumers and the fees paid to the drivers. This would not be possible without a sophisticated AI learning model behind it.

Uber Screenshot with surge pricing

3. Autonomous Navigation

On the topic of transportation, machine learning is also emerging as an enabling solution to the needs of autonomous navigation. Indoor autonomous navigation was solved a decade ago for mobile robots operating in controlled environments (such as warehouses and office buildings) without the need for artificial intelligence. The autonomous navigation of automobiles requires 10 to 100 times more processing power to function. The key problem for autonomous automobiles are both the speed at which the vehicles operate and the uncontrollability of the environment (including factors such as weather, time of day and all of the various road hazards).

Another problem for deploying machine learning systems is the large amount of training data that is required. Tesla solved the training data acquisition problem by deploying their very first models with a complete set of sensors, which captured on-road data as their first customers drove their vehicles manually. This strategy turned every Tesla driver/owner into a data acquisition device for the company. As a result, Tesla received millions of miles of real-life driving data to help train their autonomous driving AI. Other automotive companies are only now beginning to reach the same level of capability as Tesla.

Navigating autonomously is a difficult problem, due in part to the fact that the vehicles are carrying human payloads. However, autonomous trucks are successfully operating on the interstate highways, transporting cargo. Tu Simple is one company who has successfully developed an autonomous rig. All of the autonomous vehicles in operation still require a licensed human driver in attendance.

Tu Simple truck on the road

4. Recommendation Engines

If you listen to music or watch movies, then you are familiar with at least one of the recommendation engines that have emerged in the last couple of years. From Pandora for music to Netflix for movies, the use of machine learning to automate content recommendations is one of the most mature applications for AI. Both Pandora and Netflix now have millions of subscribers who use their service everyday. With that usage data, these services are able to train their machine learning models about all of the content consumed by their users. The result is that the models can predict the types of music or movies that a user might be interested in, based on their consumption patterns and the patterns of other consumers. The magic is the recommendations aren’t programmed, but rather build their intelligence over time directly from the trends that the system sees across all of the user base. Pandora uses 400 musical characteristics of each song to help the AI engine categorize a new song.

5. Home Automation

One of the exciting and emerging application areas for machine learning is home automation. I gave up a long time ago trying to get my kids to turn off lights, fans and other electrical devices in our house. However, with home automation, smart devices can learn usage patterns and then adapt themselves over time. The Nest thermometer is one of the most successful home automation solutions to adopt machine learning to predict and optimize home heating and cooling cycles. The Nest is connected to the Internet and can leverage the processing power of the cloud to compare the heating and cooling cycles of all of the Nest customers. The result is that Nest can implement the optimal heating and cooling cycles which use the least amount of electricity. When combined with weather and location data, Nest can even preemptively heat or cool your house.

Nest Thermostat

6. Face Detection

Face detection is another machine learning application which has matured in the last five years. Face detection is enabling the autonomous tagging of images on-line. The driving force for this application has been social media tools such as Facebook, Instagram, Pinterest and SnapChat. All of these social media tools operate on images shared online by their users. When users “tag” images with the names of their friends, the system learns how to associate user names with their faces. What makes this work is that the users do all of the hard work of training the system, as they manually tag their friends.

7. Navigation

Navigation is another machine learning application which has matured to the point that mobile phone users now consider it to be a utility. Mobile phones are connected to the cloud in real-time, and track the position, direction and speed of travel of the phone. This has enabled solutions such as Waze and Google Maps to use mobile phones as real-time sensors for traffic conditions and to map the paths that users take between destinations. As a result, the path data is used to train machine learning models about the optimal paths at any time of the day, and to alert all users to accidents and heavy traffic in real time.

8. Email

A decade ago, email spam was a real problem for any email user. What started as “blacklisting” of email addresses in each individual in-box, quickly evolved into an escalating war between the spammers and the email recipients. The evolution of AI, however, has changed this war. Email tools employing AI are now able to not only verify the sender’s email, but to also read the content of the email and determine if the content is spam or a legitimate email of interest to the receiver. In addition, on-line email providers like Gmail are able to categorize and sort email into folders based on the content of the email. The result is a much more efficient email experience by users and better control over unsolicited email.

9. Banking and Finance

The world of banking and finance has seen the emergence of AI as a tool to help automate many financial processes. One application which benefits both the bank and the end user is fraud protection. AI tools which monitor credit card usage in real time can now accurately predict when a transaction is fraudulent. These AI-based tools help to minimize or limit any damage or loss, which in term helps to keep rates low. In addition, credit decisions which used to take days or weeks can now be processed in seconds or minutes. This improves the consumer experience and enhances on-line commerce.

10. Grading

Finally, AI solutions are emerging to help our over worked teachers do their jobs more efficiently. AI is being deployed to help teachers evaluate and grade student work. It’s now possible to review student writing with a plagiarism checker, and to cross check online references and quoted material for accuracy. These tools can also easily rank student writing for grammar and verify writing levels.

Summary

As you can see, artificial intelligence is already being deployed to help enhance our lives in many ways. The applications which we just examined have all come to market in the last five years and are quickly maturing as consumers find them useful. As a result, the state of the art for AI and machine learning is quickly evolving and innovation is happening to help make the most difficult applications possible. We’re still far from a general artificial intelligence, but very specific applications of AI are changing our lives every day for the better.

catalog IQ demo screen

ICECREAM LABS LAUNCHES AI-BASED SOLUTION TO MAXIMIZE CATALOG DATA QUALITY MANAGEMENT

FOR IMMEDIATE RELEASE:

ICECREAM LABS LAUNCHES AI-BASED SOLUTION TO MAXIMIZE CATALOG DATA QUALITY MANAGEMENT

CatalogIQ uses machine learning to accurately validate and verify e-commerce product data to ultimately increase revenue and boost operations

SAN FRANCISCO, Calif. – June 12, 2018 – IceCream Labs, the leader in intelligent merchandising solutions for e-commerce providers, today unveiled CatalogIQ, an AI-based platform that leverages machine learning for unparalleled catalog data quality management. CatalogIQ enables companies to maximize the potential of their product catalog, resulting in immediate impacts to revenues and operations.

“When you have a product catalog with hundreds of thousands or millions of product records, it can be a daunting task to validate all of the data attributes and related image data,” said IceCream Labs founder and CEO Madhu Konety. “Product data comes in continuously from your suppliers and gets reconciled with existing product records in your product catalog. The one thing that you don’t want to do is to lose a sale because the product record is incomplete or doesn’t reflect the actual product that you’re selling. We’ve solved this problem for some of the largest e-commerce vendors in the market and are now offering a new version of our solution for e-commerce providers of all sizes.”

Introducing CatalogIQ

CatalogIQ is the foundation of the IceCream Labs Cognitive Commerce solution. With CatalogIQ, companies can ensure their product catalog data is complete and that it’s ready to use with customers. CatalogIQ is especially beneficial for product catalogs with a large number of products. Using machine learning models, CatalogIQ can adapt to changes in product data over time as users add and delete products or merge new supplier data. Regardless of what is being sold, CatalogIQ can quickly learn about product attributes and begin to validate the integrity of the meta-data.

“We’ve built machine learning models which can identify incorrect attributes and even ensure that product images accurately match the product being sold,” added Konety. “Our first customer is one of the largest US ecommerce providers, and their catalog is so large that it’s impossible for a human to validate all of the data in a timely fashion. With CatalogIQ, they are now able to stay in front of almost continuous product data changes.”

CatalogIQ enables businesses to understand the data integrity of their product catalog. CatalogIQ scans the data to identify which elements are incomplete, inaccurate or invalid. It can identify suspect duplicate product records. To help improve search results, CatalogIQ assesses the SEO-readiness of the text components of a product record.

The result is a thorough understanding of the quality of data in the product catalog. Catalog Managers can now identify all incorrect data and cleanse the information before publication. CatalogIQ gives Catalog Managers the confidence that product information is accurate.

The payback is two-fold: First, catalog managers can now validate that supplier product data is accurate and quickly isolate records which need to be cleansed. This reduces the product data integration timeline and improves the time to market for adding new products to the catalog. Secondly, catalog managers can be assured that bad data doesn’t end up in front of real customers while they are shopping, which can impact both revenue and brand reputation.

CatalogIQ is available now at www.icecreamlabs.com. Customers who leverage CatalogIQ will be able to benefit from the upcoming automation that Cognitive Commerce will enable across the entire ecommerce data management spectrum.

About IceCream Labs

IceCream Labs is the only AI powered platform which provides on-demand Intelligent Merchandising solutions for e-commerce retailers, brands and marketplace sellers. We help you realize the maximum potential of your product catalog by boosting the quality of your product content to create an immediate impact on revenues and operations.

Several of the world’s largest retailers  have benefited from our Intelligent Merchandising platform. Deep learning applications on the platform have delivered results with absolute precision, accelerating revenues 4X for our customers.

Our new catalog data quality platform consumes product data coming in through various sources. Applications on the platform continuously process and profile the content quality of over 100 million products and 50 million images empowered by our big data algorithms. This data is interpreted by our multiple patent pending Deep Learning models to cleanse, enrich and optimize your product content. The output of the models can be integrated seamlessly with your existing solutions to help you reach your business goals.

At IceCream Labs, we believe in the value of technology and its ability to disrupt traditional business models. Our Culture – like technology – is open and without any boundaries. We believe in the power of providing simplicity while managing the complexity behind it, by keeping our focus constantly on Innovation and Execution.

CONTACT:

Mike Oitzman

IceCream Labs

mike.oitzman@icecreamlabs.com

Web: icecreamlabs.com

Ph: 530-270-9466

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