Dropbox announced two new products today that (not quite shockingly) shift the company's focus to AI. Dropbox AI scans your documents, providing summaries and answers, while the more ambitious Dropbox Dash serves as a unified search bar for your life. Dropbox AI is the simpler of the two new offerings. It applies artificial intelligence to file previews, offering summaries and a natural language Q&A about your docs. "With the click of a button, you can summarize your content, like contracts and meeting recordings, into a concise explanation," the company explained. Or, ask Dropbox AI questions about the content of a specific file, and it can answer.

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The news: Falkon, a Seattle-based sales and marketing analytics startup, has raised $16 million. The tech: Falkon uses API connections to pull data from all major go-to-market data sources including Salesforce, Outreach and Google Analytics, among others. Co-founder and CEO Mona Akmal told GeekWire that it can also connect to customer data warehouses like Amazon Redshift and Microsoft's Azure. It then uses this data to create data-driven recommendations for sales and marketing teams using AI. The team: The company was founded in late 2019 by a trio of tech vets.

What are the use cases for Python? Here are 11 real-world applications for Python across a wide variety of industries and businesses.

In the computing world we have gone through several phases. In the beginning organisations usually had one large mainframe computer. This was followed by the era of dumb terminals and eventually by the era of personal computers – the first-time end-users owned the hardware that did the processing. Several years later cloud computing was introduced. People still owned personal computers, but now accessed centralised services in the cloud such as Gmail, Google Drive, Dropbox, Trello, and Microsoft Office 365.

Image classification lets us automatically understand what's in an image, but by itself this isn't enough to enable search. Sure, if a user searches for beach we could return the images with the highest scores for that category, but what if they instead search for shore? What if instead of apple they search for fruit or granny smith? We could collate a large dictionary of synonyms and near-synonyms and hierarchical relationships between words, but this quickly becomes unwieldy, especially if we support multiple languages. Word vectors So let's reframe the problem.

Some people are interested to Python but might still have questions in their mind, are there any big companies who uses Python programming. They are concern whether is it worth it to learn Python? Python is the fastest growing programming language on the planet. It is also the most wanted programming language by developers in 2017, 2018, and 2019. Aside from its simplicity, Python is well-known as a multi paradigm programming language. Programmers are freely to develop their programs using different approaches, including object-oriented programming, functional programming or procedural programming. This makes so many tech companies love Python and use it to their real world applications.

Infrastructure for Contextual Bandits and Reinforcement Learning -- theme of the ML Platform meetup hosted at Netflix, Los Gatos on Sep 12, 2019. Contextual and Multi-armed Bandits enable faster and adaptive alternatives to traditional A/B Testing. They enable rapid learning and better decision-making for product rollouts. Broadly speaking, these approaches can be seen as a stepping stone to full-on Reinforcement Learning (RL) with closed-loop, on-policy evaluation and model objectives tied to reward functions. At Netflix, we are running several such experiments.

As we laid out in our blog post introducing DBXi, Dropbox is building features to help users stay focused on what matters. Searching through your content can be tedious, so we built content suggestions to make it easier to find the files you need, when you need them. We've built this feature using modern machine learning (ML) techniques, but the process to get here started with a simple question: how do people find their files? What kinds of behavior patterns are most common? Starting with this basic understanding of the kinds of files users access, we built a system using a set of simple heuristics--manually-defined rules that try to capture the behaviors we described above.