Google is borrowing a few cues from Instagram and Pinterest to encourage more shopping in its search results. The internet giant is testing shoppable ads within image searches -- find a picture of your ideal desk and you can tap a shopping tag button to see basic details as well as a link to buy it. This only applies to sponsored ads, thankfully, so you don't have to worry about ads covering the images you want to see. The test is currently visible only to a "small percentage" of users who search for certain broader topics, such as home offices and abstract art. Whether or not it expands will likely depend on early results.

In this episode of our Inside the Strategy Room podcast, senior partner Tamim Saleh cuts through the hype around artificial intelligence (AI) and offers clear guidance for executives looking to make precise strategic decisions about where and how to employ AI in their businesses. Tamim shares insights on the impact of machine vision on AI, the future of voice recognition, and the latest developments in advanced analytics, virtual assistants, and robotics. He outlines the challenges companies face when adopting AI and the steps CEOs can take to overcome them. Tamim is a senior partner in our London office, and he is with me at our Global CFO Forum, where he's speaking about AI and machine learning. Tamim, one of the things you've talked about is the notion of five different developments of AI. Tamim Saleh: Machine learning and AI are limited by the fact that when we input data as humans, first of all we are slow, and we make mistakes. One of the fastest-growing technologies is capturing data through image analytics and cameras. And the beauty of this is, cameras don't make the same mistakes we do, because they capture things the way they are, and they don't see the world the same way that we do. In fact, the spectrum is much wider than what we see. It includes infrared, et cetera. So there are a lot of business problems [that image technology can help].

Image recognition and classification is a rapidly growing field in the area of machine learning. In particular, object recognition is a key feature of image classification, and the commercial implications of this are vast. These are just a few of many examples of how image classification will ultimately shape the future of the world we live in. So, let's take a look at an example of how we can build our own image classifier. VGG16 is a built-in neural network in Keras that is pre-trained for image recognition.

Microsoft is adding a very useful feature to its Excel mobile apps for iOS and Android. It allows Excel users to take a photo of a printed data table and convert it into a fully editable table in the app. This feature is rolling out initially in the Android Excel app, before making its way to iOS soon. Microsoft is using artificial intelligence to implement this feature, with image recognition so that Excel users don't have to manually input hardcopy data. The feature will be available to Microsoft 365 users.

However, I've observed that some actually lack the necessary mathematical intuition and framework to get useful results. This is the main reason I decided to write this blog post.

AI technology can be used by the government to learn from historical data, analyze new inputs to perform human tasks, such as pattern recognition from data, image and video analytics which human analytics can miss due to large scale and complexity of data with exponential speed. Machine learning and deep learning can find insights hidden in data without necessarily being programmed where to look or what to find which results in better, faster and more accurate decision-making capabilities. NLP is used to improve understanding and interaction between humans and machines, automatically extracting new emerging trends from large amounts of structured and unstructured data which can result in the discovery of various drawbacks and solutions. Computer vision analyzes and interprets what's in an image or video through image processing, image recognition and object detection which helps significantly for proper surveillance of cities. AI with tools like machine learning and big data analytics can help improve lifestyle of citizens by optimizing traffic conditions, reducing pollution, energy saving and energy generation, safety and privacy, improving public health, enhancing agricultural outcomes, smart transportation, infrastructure optimization, smart homes, instant emergency solutions, natural resources management and accurate weather are among the many factors government can use AI for enhancing life quality of citizens. AI can be used to do financial analysis from multiple transaction mediums for taxation purposes.

ViSenze, an image recognition startup that delivers visual search tools designed for online retailers such as ASOS and Rakuten, recently revealed that it had secured $20 million in a Series C funding round. Both Sonae IM and Gobi Ventures co-led the financing round, which also included the participation of other investors, including returning backers WI Harper and Rakuten. Established back in 2012, ViSenze has so far raised a total of 34.5 million dollars (its previous funding round was a Series B held in September 2016). ViSenze customers include the likes of Uniqlo, Zalora, and Urban Outfitters, who bill the Singapore-based company's software portfolio as a "personal shopping concierge" that helps shoppers in finding or discovering products based on automatic photo tagging, visual search, and suggestions based on their browser history. The company's verticals include intellectual property, furniture, jewelry, and fashion.

Clarifai's API is another image recognition tool that doesn't require any machine learning knowledge prior to implementation. It can recognize images and also perform thorough video analysis. A user can start to make image or video predictions with the Clarifai API after they specify a parameter. For example, if you input a "color" model, the system will provide predictions about the dominant colors in an image. You can either use Clarifai's pre-built models or train your own one.

Student and freelance AI / Big Data Developer with a passion for full stack. In this article, I apply a series of natural language processing techniques on a dataset containing reviews about businesses. After that, I train a model using Logistic Regression to forecast if a review is "positive" or "negative". The natural language processing field contains a series of tools that are very useful to extract, label, and forecast information starting from raw text data. This collection of techniques are mainly used in the field of emotions recognition, text tagging (for example to automatize the process of sorting complaints from a client), chatbots, and vocal assistants.