OpenAI released ChatGPT just a few short months ago, and it's fair to say that it took the world by storm: It has over 100 million active users already. No wonder, when it can generate human-like, grammatically correct responses. Related technologies can also produce artwork and code by entering a description of what you want, and the tech produces it. You can even interact with the AI after your initial question, so if you don't like the output you got or need clarification, you can ask additional questions or make adjustments to your picture or code, so it more closely matches your vision. All of this happens instantly without the help of a subject expert, an artist or a coder.

You save tons of time and effort creating dynamic visualizations even without any design experience or skills. Sharing and publishing your charts is a breeze since you can download your visualizations as JPG or PNG and embed them on your website. Those are the best Google Sheets add-ons to use for reporting. Now let's move on to the five reliable apps for data analysis. Google Sheets add-ons for analysis 6. Statistical Analysis Tools Statistical Analysis Tools is a Google Sheets add-on package containing functions designed to automate the generation of statistical analysis techniques. The app works almost exactly like the MS Excel Analysis ToolPak, but it includes a few enhanced features, such as dynamic results and speed performance. This add-on is equipped with tools, including: Exponential Smoothing Descriptive Statistics t-Test: Paired Two Sample for Means f-Test: Two-Sample for Variances z-Test: Two Sample for Means Analysis of Variance (ANOVA): Single-Factor Analysis of Variance (ANOVA): Two-Factor (without replication) Open your spreadsheet with your dataset, select your desired statistical analysis technique from the add-on, fill out the parameters, and you're good to go. With the app, you won't need to manually input functions and formulas to your dataset to get your desired calculations and values, streamlining your data analysis.

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This automatically-generated transcript is taken from the IT Pro Podcast episode'Do we need AI regulation?'. To listen to the full episode, click here. The AI industry has been going from strength to strength over the past several years, with machine learning technology becoming increasingly widely available to businesses, along with a stream of breakthroughs in research and development. However, this explosion of AI capabilities has also brought its share of problems. Questions of model transparency, implicit bias and ethical deployments have frequently been levelled at efforts in this space. And numerous campaigners have called for governments to introduce legislation, which will place greater controls on the development and implementation of AI systems. Joining us this week to discuss the issue of AI regulation, whether it's necessary and how it might be implemented without stifling innovation is Cindi Howson, chief data strategy officer for analytics software vendor ThoughtSpot. Cindi, great to have you on the show. Great to be here, Sabina, and Adam.

On a global scale, people are estimated to generate 463 exabytes (one exabyte one billion gigabytes) of data each day by 2025. To put that in context, at the start of 2020, approximately 44 zettabytes of data (one zettabyte one trillion gigabytes) existed overall. If companies tried to shift through this data on their own, it would be an unmanageable task. But artificial intelligence (AI) makes it possible because AI models can work much faster than humans, and they don't require breaks. While humans can do most of what AI can in business intelligence, the main benefits of using AI are speed, consistency, and accuracy.

ThoughtSpot has expanded the number of backend data sources that can be accessed via its cloud-based analytics platform to include the Databricks cloud service based on the Apache Spark framework. A ThoughtSpot for Databricks offering now makes it possible to directly run queries through the ThoughtSpot search engine against a Databricks Lakehouse, a data architecture that combines the features of data lakes and data warehouses, according to Databricks. For nearly a decade, ThoughtSpot has been making the case for an alternative approach to analytics that eliminates the need to rely on a data analyst or IT professional to construct a dashboard. Instead, it presents end users with a search interface through which they can employ natural language to query multiple backend data repositories. That approach enables end users to interrogate data in a more interactive fashion that is not constrained by the limitations of how a dashboard was constructed, said Seann Gardiner, senior vice president of business development for ThoughtSpot.

VoiceBase, the leading AI-Powered Voice Analytics company, announced a strategic partnership with ThoughtSpot, the leading Search & AI-Driven Analytics platform, to revolutionize how enterprises gain faster insights from their voice data through innovative search capabilities. Voice Analytics has been empowering frontline workers in contact centers and organizations for years, but 86% of businesses report that they need better technology-enabled insights to really gain value from their data. The value of these analytics can be transformational for enterprises. In fact, "By 2025, AI for video, audio, vibration, text, emotion and other content analytics will trigger major innovations and transformations in 75% of Fortune 500 global enterprises" according to Gartner's Top 10 Trends in Data and Analytics, 2020 report. By partnering with ThoughtSpot, VoiceBase customers can equip their business users to find powerful, contextual insights in their voice, text, and other contact center data.

Banks and insurance firms are planning to increase their artificial intelligence-related investment into technology by 2025, according to research from The Economist Intelligence Unit. The report, commissioned by AI-analytics and search firm ThoughtSpot, surveyed 200 business executives and c-suite leaders at investment banks, retail banks and insurance companies in North America, Europe and Asia Pacific. It found that while a large majority (86 per cent) of respondents had a strong degree of confidence in the benefits of AI to shape the future of financial institutions, more than half of respondents said the technology was not yet in use in the business' processes and offerings, with just 15 per cent saying the technology is used extensively across the organisation. However, despite relatively low levels of implementation, the research found that many institutions are beginning to invest in AI over the next five years, with 27 per cent saying it will spur new products and services, a quarter believing it will open up new markets or industries and the same amount saying it is paving the way for innovation in their industry. Looking to the future, 29 per cent of respondents expect between 51 per cent and 75 per cent of their workloads to be supported by AI technologies in five years' time, as processes become increasingly automated.

The convergence of machine learning and business intelligence is upon us, as BI tool makers increasingly are exposing ML capabilities to users, and users are performing ML activities in their BI tools. That's according to the latest Gartner report on analytics and BI tools, which was released this week. In its February 11 Magic Quadrant for Analytics and Business Intelligence (ABI) Platforms, the storied Stamford, Connecticut analyst firm did its best to quantify and qualify the trends in the sector. While BI and ML have largely existed on parallel tracks, with BI seeking to report what happened and ML seeking to predict what will happen, Gartner sees the two disciplines converging, at least as far as the toolsets are concerned. Not all ML work will occur within BI tools, of course.

Stop chasing the wrong candidates and start engaging with the right ones -instantly. Instant pipelines of candidates who are interested in your company, are highly qualified and are the right fit for the team that they would work on, is the promise of Artificial Intelligence when applied to recruiting and talent acquistion tasks. Most recuriters use various mediums to search for that ideal or'perfect' candidate. While selecting the right candidate is important, so is keeping time and cost-to-hire down. The Eightfold Talent Intelligence Platform (TIP) predicts "what's next" for candidates to allow companies to accelerate their hiring.