Articles

Top 5 Data Science, AI, And ML Trends for 2022

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The global artificial intelligence market is expected to reach over $200 billion by 2027. The big data market segment is anticipated to grow up to US$103 billion by 2027 with a share of 45% from the software segment. Similarly, the projected size of the global deep learning market will reach over $40 billion by 2027 at a CAGR of 39.2%.

Indeed, the implementation of technologies like data science, artificial intelligence, and machine learning in organizations has increased exponentially. In the last two years, during the pandemic outbreak, the technologies played a crucial role in saving lives and fostering economic resilience, showing many surprising trends. 

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Integrating Alexa With Microsoft LUIS

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Alexa has a very good Natural Language Processing engine. However, there are other NLP engines in the market that can be used and those are including more and more capabilities.

  • Integrating Alexa with Microsoft LUIS
    • Prerequisites
    • Preface
    • Setting up our Alexa Skill
    • Creating Azure Cognitive Services
    • Creating MS LUIS App
    • Calling MS LUIS from Alexa Skill
    • Final Result
    • Resources
    • Conclusion

Prerequisites

Here you have the technologies used in this project

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Generating Business Insights Using Advanced Analytics

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Machine learning-based applications have seen significant commercial success in several mainstream consumer applications in the recent past. Self-driving cars, stock-trading bots, robo-advisors, Amazon’s Alexa, and Apple’s Deep Fusion and Siri are some of the renowned examples of commercial success with artificial intelligence and machine learning. AI has also made our lives easier by improving the customer experience of the products we use. Google’s text generation software, Netflix’s recommendation engine, and Facebook and Twitter’s fake news detection are other prime examples. In fact, every single technology company uses AI in its mainstream applications either directly or indirectly. Non-technology companies are also using AI to improve customer experience, improve efficiency, and generate new revenue streams. Chatbots, robo-advisors, systems that predict system failures, and products that generate efficient supply chain routes are some of the prominent ways in which non-technology companies use AI. This is leads to a popular belief that AI and ML are primarily used by technology companies or they are being used by non-tech companies to build AI-based products.

This popular perception is not true. There are plenty of avenues in which AI/ ML is being used or can be used by non-tech and non-product-based groups to generate insights. In this article, I am going to share with you four ways in which you can augment advanced analytics into your analytics strategy to generate insights.

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How Machine Learning Has Disrupted Finance Industry

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In the last decade, the finance industry has seen an infusion of cutting-edge technologies like never before. This transformation is largely attributed to many startups that appeared on the scene post 2008 recession and followed a technology-first approach to create financial products and services with a target to improve customer experience. FinTech, as these startups are known, have been the early adopters of the new technologies like Smartphones, Big Data, Machine Learning (ML), Blockchain and were considered the trendsetters that were later followed by more traditional banks and financial institutes.

The recent advancements in machine learning and deep learning has really pushed the boundaries of computer vision and natural language processing. FinTechs are leaving no stones unturned to capitalize on these breakthroughs to improve financial services. As per a report, the ML Fintech market was valued at $7.27 billion in 2019 and it is expected to grow to $35.40 billion by 2025. Statista forecasts that the entire banking industry overall will be able to derive the business value of  $182 billion globally with machine learning by the year 2025.

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AI That Can Warn People of Approaching Tow Trucks

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Two years ago, our  team started developing and training a neural network for recognizing cars in parking lots. During this time, we have collected a dataset of more than 26 thousand images, connected 376 cameras to the car recognition service, 122 parking lots, of which only 5400 parking spaces. We have developed a mobile application displaying free and occupied parking spaces, and also created an SpotVision API that anyone can use to solve business problems.

 

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OpenAI GPT-3: How It Works and Why It Matters

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You have probably heard about an innovative language model called GPT3. The hype is so overwhelming that we decided to research its core and the consequences for the tech players. Let’s explore whether the language deserves this much attention and what makes it so exceptional.

What Is GPT-3? Key Facts

GPT-3 is a text generating neural network that was released in June 2020 and tested for $14 million. Its creator is the AI research agency OpenAI headed by Sam Altman, Marc Benioff, Elon Musk, and Reid Hoffman.

The language is based on 175 million parameters and is by far more accurate than its predecessors. For example, GPT-2 had only 1.5 billion of parameters, and Microsoft Turing NLG – 17 billion of them. Thus, the power of GPT-3 is significantly surpassing the alternatives.

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6 Pillars of a Successful AI Strategy

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Machine learning and artificial intelligence, in general, have been on everyone’s lips for some time now. While the topic of AI is in the foreground in the media, most people (especially the management) still don’t know how machine learning is best applied.

Ultimately, machine learning can be described as a synergetic relationship between man and machine. Machine learning in practice requires the application of the scientific method and human communication skills. Successful companies have the analytical infrastructure, know-how, and close collaboration between analysts and business professionals to translate these synergies into ROI.

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Why Most Chatbots are Annoying and How to Make Sure Yours Isn’t

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As conversational language interfaces begin to dominate customer service, so does the backlash against chatbots grow. Forrester predicted last year that 2019 would be the year of the backlash against inefficient chatbots, and it looks like they were right. For example, a survey commissioned by an open software service company Acquia, that analyzed responses from more than 5,000 consumers and 500 marketers in North America, Europe and Australia, found that 45 percent of consumers find chatbots “annoying.”

At the same time, the importance of conversational AI for business today cannot be overestimated. When done right, conversational AI has the ability to significantly increase your competitive advantage and fundamentally change the nature of business-customer interaction.

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Parallel Grid Search in H2O

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H2O is, at its core, a platform for distributed, in-memory computing. On top of the distributed computation platform, machine learning algorithms are implemented. At H2O, we design every operation, be it data transformation, training of machine learning models, or even parsing to utilize the distributed computation model. In order to work with big data fast, it’s necessary.

However, a single operation usually can not utilize clusters’ computational resources to the very maximum. Data needs to be distributed across the cluster, and many operations require sequential execution of tasks, which, even if implemented in a distributed manner, follow after each other and require data exchange. These and many other smaller factors, if summed up together, may introduce a significant overhead.

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Build Your First Python Chatbot Project

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Introduction

Chatbots are extremely helpful for business organizations and also the customers. The majority of people prefer to talk directly from a chatbox instead of calling service centers. Facebook released data that proved the value of bots. More than 2 billion messages are sent between people and companies monthly. The HubSpot research tells us that 71% of people want to get customer support from messaging apps. It is a quick way to get their problems solved so chatbots have a bright future in organizations.

Today we are going to build an exciting project on Chatbot. We will implement a chatbot from scratch that will be able to understand what the user is talking about and give an appropriate response.

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