Assassin GPT or Saviour GP

First, it came for designers; then, it came for writers, and now it seems to be targeting software developers. The recent launch of the code interpreter plugin has sent shockwaves through the data science community, putting their jobs at risk. It appears that ChatGPT is eliminating job categories one by one, expanding its threat. Should we still call it ChatGPT or rename it AssassinGPT?

Instead of grappling with complex spreadsheets and visualisation software, the code interpreter plugin by ChatGPT allows users to simply prompt the interpreter to get the desired results.?

OpenAI describes it as a "very eager junior programmer working at the speed of your fingertips." The plugin supports file uploads and offers downloadable outputs. It excels in solving mathematical problems, file conversions, data analysis, and visualisation. Furthermore, it provides access to various Python libraries, including OCR and Matplotlib, enhancing its capabilities.

Users are leveraging the plugin effectively, using it to analyse datasets from Netflix shows to identify trends and generate visualisations. For instance, a user created a visualisation of every lighthouse in the United States based on a simple CSV file of lighthouse locations.

From a paranoid perspective, the entire development may seem unsettling. However, the reality is not as gloomy as portrayed in the media. Data scientists have responsibilities that go beyond data wrangling and visualisation. They understand the importance of storytelling with data and uncovering insights through human intuition. Unfortunately, ChatGPT's code interpreter lacks logical thinking and falls short in fulfilling these tasks. Additionally, the plugin introduces the challenge of hallucinations.

Considering its limitations and inconsistencies, ChatGPT still has a long way to go before surpassing human capabilities. However, with its rapid progress, it may soon catch up and emerge as the winner. If not the present, the future is definitely dicey.

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Beware Tech Giants! Watson is Here

IBM has entered the generative AI space with WatsonX, offering enterprise-focused AI tools. IBM aims to revive its reputation with a focus on customization and narrow use cases. Watson Code Assistant, part of WatsonX, increases developer productivity for IT automation and expands into content discovery and code explanation.?

IBM faces competition from Microsoft, NVIDIA, and AWS, but its early investment in quantum computing gives it an advantage. IBM believes the convergence of cloud, AI, and quantum computing will lead to significant advancements in the coming decade.

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OpenAI is the Real Winner

Google seems to feel relaxed when it comes to competitor OpenAI. In a leaked internal document, the tech giant has found to be discussing that OpenAI can’t maintain an edge over Google.?

But the reality is completely different. OpenAI is a big craze among companies. Even Google-backed startups are using GPT-4 instead of using Google’s PaLM-2. For instance, Cohere and Anthropic, both of which are backed by Google, will use GPT technology on Salesforce’s platform.?

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Lakes vs Mesh

Data lakes have become essential components of modern data infrastructure, offering benefits such as storing large amounts of raw data and providing secure access. However, as the market grows rapidly, enterprises are realising the drawbacks of monolithic data lakes, including complexity, data quality issues, and security concerns.?

To address these challenges, a new approach called data mesh is emerging, where domain-specific data lakes replace the centralised monolith. While data mesh offers advantages, its benefits are more apparent at larger scales, and companies should evaluate their specific needs when choosing a data architecture.

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