My Cookie Mix

L'utilizzo dell'intelligenza artificiale nelle previsioni meteo non è una novità. Ibm, che nel 2016 ha acquistato per due miliardi di dollari The Weather Company, una delle più importanti società del settore, e l'ha piazzata sotto l'ombrello della sua Ai, Watson, ricorda che si fa ricorso agli algoritmi dal 1970. Tuttavia, ricordano dall'Ecmwf, il Centro europeo per le previsioni meteo di medio termine (che pure lavora sul tema dal 2018), grandi passi in avanti sono stati compiuti tra febbraio 2022 e aprile 2023, con una serie di ricerca sull'accuratezza delle previsioni meteo da parte di sistemi di machine learning da parte di Huawei, Nvidia e Google Deepmind. D'altronde, quello delle previsioni meteorologiche è un settore che per Expert market research, un centro studi di mercato, nel 2027 arriverà a valere 3,85 miliardi di dollari (con un tasso di crescita annua dal 2022 del 5,6%). Leggi tutto:  https://www.wired.it/article/meteo-previsioni-intelligenza-...

Se però gli esempi di successo dell’utilizzo delle AI aumentano, anche i fallimenti di questo strumento cominciano a riempire le pagine dei giornali: Samsung ad esempio ha visto diffondere parte del proprio codice sorgente in rete quando alcuni dei suoi dipendenti lo hanno inserito in ChatGPT per ottimizzarlo, ed abbiamo visto la prima minaccia di azione legale per diffamazione verso una AI, quando famoso chatbot ha accusato erroneamente di corruzione un Sindaco australiano. ... È in questo contesto quindi che nasce il concetto di RAI - Responsible Artificial Intelligence. Se di fatto la rapidissima evoluzione delle AI non si sposa molto bene con un utilizzo responsabile delle stesse, e se allo stato attuale delle cose non è responsabilità diretta degli sviluppatori vigilare sull’etica e sulla responsabilità oggettiva dei propri Bot (perchè in effetti sarebbe limitante per uno strumento che deve “imparare ad apprendere”), è indispensabile per le aziende costruire dei framework in cui ...

In this episode of McKinsey Talks Operations, recorded at McKinsey’s recent CFO forum in partnership with Inside the Strategy Room, host Daphne Luchtenberg talks with Matt Jochim and Martin Rosendahl, partners in McKinsey’s London office, along with Samdruk Thomas, an associate partner based in Zurich. Together, they delve into the challenges and opportunities faced by sales, general, and administrative (SG&A) functions in today’s fast-moving economy. Discover how courageous leadership by CFOs can balance short-term cost pressures with long-term innovation, leveraging digital technologies such as generative AI, and how a clear strategic road map can deliver a successful transformation. Their conversation has been lightly edited for clarity. Leggi tutto:  https://www.mckinsey.com/capabilities/operations/our-insights/reset-and-reimagine-the-role-of-generative-ai-in-sg-and-a-success

Data scientists and business users quite often exchange spreadsheets as a form of collaboration. Right now, this is an awkward process; while there are a variety of libraries that allow Python to read from and write to spreadsheets, these tools are complicated to use, especially on the business stakeholder side of the exchange. In practice, data scientists have to do a bunch of manual work to update spreadsheets and make collaboration possible. While no tool can magically resolve human communication problems, Python in Excel will create a common working platform for data scientists and spreadsheet users, dramatically streamlining the collaboration process. When tools like Slack, Git, or Asana are used well, they create a sense of seamless coworking on a common problem. Python in Excel has the potential to deliver a similar experience, finally allowing us to move beyond the throw-it-over-the-wall mentality that is common right now. Data scientists still need to stop by the desks of ...

Today, Anaconda and Microsoft announced a groundbreaking innovation: Python in Excel. This marks a transformation in how Excel users and Python practitioners approach their work. For Excel users, this opens a new world of data analysis potential previously limited to data scientists and developers. Within your familiar spreadsheet environment, you can now harness Python’s power to perform complex statistical analyses with popular packages such as pandas and statsmodels and create sophisticated visualizations using Matplotlib and Seaborn. Python practitioners can now marry scripts and rich visualizations with the widespread accessibility of Excel, enabling an uninterrupted workflow and making your work easier to share with colleagues who primarily use Excel. The integration of the Anaconda Distribution for Python in Microsoft Excel is more than a feature; it’s a redefinition of what’s possible within data analytics. It combines Python’s advanced capabilities with Excel’s familiar in...

‘Data’ is a word that has been around for a long time. Data is essential for decision-making today when a staggering 2.5 trillion bits of data are produced daily. However, how do you believe we can manage that much data? The function of a data analyst is one of several important roles in the marketplace today that work with data to obtain insights. A data analyst has to use various techniques to extract insights from data. The top tools that any aspiring data analyst to an experienced professional must master by 2023 are covered in this post on the 10 most popular data analytics tools. Read more:  https://www.analyticsinsight.net/top-10-data-analytics-tools-for-2023/

Microsoft partnered with Python analytics repository Anaconda to bring libraries like Pandas, Statsmodels, and Matplotlib into Excel. Python in Excel runs on Microsoft’s cloud servers, and the company is touting the security that should offer. Python runs in isolated containers, with no access to devices, your network, or user tokens, Microsoft states. Python and Excel can only really talk to each other through limited functions—xl() and =PY()—that can only return code results, not macros, VBA code, or other data, Microsoft claims. Read more:  https://arstechnica.com/information-technology/2023/08/you-can-now-wield-python-analytics-inside-excel-but-microsoft-says-its-safe/

Football or Soccer is one of the world’s most famous sports with the most incredible fan following. For context, approximately 1.5 billion people viewed the recent world cup final at the end of 2022. So what makes football more entertaining than other sports? The answer is simple. Football is unpredictable. At any moment in the game, a player can score and completely turn the game around, take Mbappe’s brilliant goals in the world cup final, which completely changed the game’s tide. Despite such unpredictability, you’d be surprised to know that you can indeed predict the outcome of a football game to a particular accuracy. This is usually done through machine learning, and thus, I will be exploring how various machine learning models can be harnessed to achieve my aim of predicting the outcome of football games! Leggi tutto: https://python.plainenglish.io/creating-machine-learning-models-to-predict-football-game-outcomes-70b6bf02885c