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Dangerous Monkey steals spot as streaming’s most most watched sequence

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Dangerous Monkey steals spot as streaming’s most most watched sequence


The crime comedy Dangerous Monkey starring Vince Vaughn shortly turned the most well-liked sequence on streaming, in line with analysts. The Apple TV+ present follows a former detective turned restaurant inspector fixing an oddball homicide in hopes of getting his outdated job again.

And it’s not Apple’s solely hit. The Instigators continues to drag in viewers, as do two extra Apple TV+ sequence.

Dangerous Monkey does good

From the beginning, Dangerous Monkey brings three strengths. It’s based mostly on finest vendor, and the sequence was tailored for Apple TV+ by Invoice Lawrence, who created just a little present known as Ted Lasso … you might need heard of it. Plus the brand new sequence stars Vince Vaughn, who’s clearly excellent for this position.

That’s sufficient to get the exhibit proper. It premiered solely every week or so in the past, however already ranks No. 1 on Reelgood’s listing of Prime 10 TV exhibits on streaming throughout the week ending August 18. And it additionally sits at No. 1 on JustWatch‘s listing of prime streaming exhibits for a similar week.

Most popular streaming series August 24, 2024
Dangerous Monkey jumped to the highest of the rankings charts, plus Presumed Harmless and Time Bandits did effectively, too.
Chart: JustWatch/Reelgood

Estimates from corporations like JustWatch and Reelgood are crucial as a result of streaming providers hardly ever reveal how many individuals tune in to their choices.

Critics additionally assume Dangerous Monkey affords good issues. It has a 92% approval score from the review-aggregation web site Rotten Tomatoes.

The Occasions (UK) reviewer mentioned, “Vaughn has an ideal car right here, and should you miss Bruce Willis on display screen, as all of us do, there are moments Vaughn is a convincing lookie-likie, solely with a bit extra hair.” And the Chicago Solar-Occasions mentioned, “Dangerous Monkey succeeds in capturing the humorous, juicy parts of Carl Hiaasen’s South Florida homicide thriller.”

Apple TV+ itself says:

“Primarily based on Carl Hiaasen’s New York Occasions bestselling novel and enduring cult favourite, Dangerous Monkey tells the story of Andrew Yancy (Vaughn), who has been bounced from the Miami Police Division and is now a well being inspector within the Keys. However after stumbling upon a case that begins with a human arm fished up by vacationers, he realizes that if he can show homicide, he’ll be again in. He simply must get previous a trove of Floridian oddballs and one dangerous monkey.”

The primary three episodes of Dangerous Monkey will be watched now, and the third seems on Wednesday. New episodes air on successive Wednesdays by way of October 9.

Watch on Apple TV

Don’t overlook The Instigators, Presumed Harmless and Time Bandits

The Instigators, a buddy motion comedy starring Matt Damon and Casey Affleck, is one other Apple TV+ hit. Its two stars play thieves stealing from a corrupt politician … and all the pieces potential going fallacious. The film is at No. 4 on ReelGood’s most up-to-date listing of hottest Prime 10 TV movies on streaming, and at No. 6 on JustWatch’s equal listing.

The Instigators is able to watch now on Apple TV+.

Watch on Apple TV

Most popular streaming films August 24, 2024
The Instigators had a reasonably robust second week.
Chart: JustWatch/Reelgood

And there are extra Apple TV+ sequence that qualify as hits. Presumed Harmless‘s season finale got here in July however the crime drama simply retains pulling in viewers. Actual endurance put it on the rankings charts each week since its debut in mid-June. Most just lately, it sat at No. 6 on each ReelGood’s and JustWatch’s lists of hottest Prime 10 TV exhibits on streaming.

All eight episodes of Presumed Harmless season one can be found now.

Watch on Apple TV

And the hits preserve coming. The streaming service turned the 1981 cult basic film Time Bandits right into a comedic journey sequence for all ages starring Lisa Kudrow, and it was at No. 8 on Reelgood’s listing of standard exhibits final week.

Time Bandits premiered July 24 on Apple TV+. Two episodes every week dropped after that, so all eight are actually prepared to look at.

Watch on Apple TV

Watch these and extra on Apple TV+

Watching Dangerous Monkey, The Instigators, Presumed Harmless, Time Bandits, plus many different sequence or movies comes with a subscription to Apple TV+. The service is $9.99 per thirty days with a seven-day free trial. You can too get it by way of any tier of the Apple One subscription bundle.

And Apple’s streaming video service additionally contains far more, after all. There’s a library of drama, comedies, musicals, kids’s exhibits, nature documentaries, and so on.



Our Finish-To-Finish Strategy to AI Privateness on Android

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Our Finish-To-Finish Strategy to AI Privateness on Android


Your smartphone holds numerous your private data that can assist you get issues achieved day-after-day. On Android, we’re seamlessly integrating the most recent synthetic intelligence (AI) capabilities, like Gemini as a trusted assistant – able to dealing with life’s important duties. As such, guaranteeing your privateness and safety on Android is paramount. As a pioneer in accountable AI and cutting-edge privateness applied sciences like Personal Compute Core and federated studying, we made certain our method to the assistant expertise with Gemini on Android is aligned with our current Safe AI framework, AI Rules and Privateness Rules.

We’ve all the time safeguarded your knowledge with an built-in stack of world-class safe infrastructure and know-how, delivering end-to-end safety in a method that solely Google can. From privateness on-device when dealing with delicate knowledge to the world’s finest cloud infrastructure, listed below are six key methods we maintain your data personal and guarded.

  • We don’t hand off to a third-party AI supplier.

    Gemini Apps may also help you with advanced, private duties from creating exercise routines to serving to you get began on a resume. And it does the laborious give you the results you want all inside Google’s ecosystem. The core processing is finished by Gemini inside Google’s safe cloud infrastructure and there are not any handoffs to third-party chatbots or AI suppliers that you could be not know or belief.

  • On-device AI privateness for delicate duties, even when offline.

    For some AI options, like Summarize in Recorder on Pixel, that profit from further knowledge privateness or processing effectivity, we make the most of on-device AI. Gemini Nano, the primary multimodal mannequin designed to run on cellular gadgets, delivers on-device AI processing for a few of your most delicate duties with out counting on cloud connectivity. You may get pleasure from options like summarizing textual content even while you’re offline.

  • World-class cloud infrastructure that’s safe by default, personal by design.

    For AI duties that use knowledge already within the cloud or have advanced calls for that require extra processing energy than what’s doable on-device, we use Google’s extremely safe cloud infrastructure. Backed by Google’s world-class safety and privateness infrastructure and processes, these knowledge facilities profit from the identical sturdy defenses which have stored Google merchandise secure for billions of customers for greater than 20 years. So you may ask Gemini to search out particulars in your lease settlement saved in your Google Drive and that knowledge is protected by superior monitoring in opposition to unauthorized entry or misuse. We additionally implement strict software program provide chain controls to make sure that solely permitted and verified code runs in our cloud atmosphere.

  • Management the way you work together with Gemini Apps.

    We have designed the Gemini Apps expertise with cautious consideration for a way you entry it and management it. You may assessment and select the Gemini experiences you need – when and the place they’re wanted most. And at any time, you may assessment your Gemini Apps chats, pin them, or delete them. Android additionally offers you management over how apps resembling Gemini reply when your system is locked.

  • Pioneering new privateness applied sciences.

    We’re all the time working to develop new methods to maintain your knowledge much more personal and safe. We proceed to innovate in advancing privacy-preserving applied sciences, like sealed computing know-how, which can be utilized to course of delicate workloads for enhanced privateness in a safe cloud enclave. Sealed computing ensures nobody, together with Google, can entry the info. It may be considered extending the consumer’s system and its safety boundaries into our cloud infrastructure, offering a digital smartphone within the sky.

  • A brand new stage of transparency.

    Transparency is in Android’s open-source DNA. Android binary transparency already permits anybody to confirm the working system code in opposition to a transparency log to make sure it hasn’t been tampered with, very similar to matching fingerprint biometrics to substantiate somebody’s identification. Binary transparency is prolonged in sealed computing environments to incorporate reproducible builds. This ensures anybody can rebuild the trusted firmware base and confirm that the ensuing binaries match what’s remotely attested as working in manufacturing and revealed in public transparency logs.

Our Dedication to Safeguarding Your Information

Similar to with all Google merchandise, we consider it’s best to be capable to get pleasure from the advantages of Android with out having to fret about safety and privateness. That is why we make investments a lot in constructing world-class protections into our services and products from the beginning. We stay up for persevering with to make AI useful and intuitive, permitting you to deal with what issues most, whereas we handle safeguarding your knowledge.

Hold a lookout for extra details about our end-to-end method to AI privateness in an upcoming whitepaper.


Unparalleled Productiveness: The Energy of Cloudera Copilot for Cloudera Machine Studying

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Unparalleled Productiveness: The Energy of Cloudera Copilot for Cloudera Machine Studying


Posted in Enterprise |
June 24, 2024 2 min learn

Within the fast-evolving panorama of information science and machine studying, effectivity is not only fascinating—it’s important. Think about a world the place each knowledge practitioner, from seasoned knowledge scientists to budding builders, has an clever assistant at their fingertips. This assistant doesn’t simply automate mundane duties however understands the intricacies of your workflows, anticipates your wants, and dramatically enhances your productiveness at each flip. Welcome to the period of Cloudera Copilot for Cloudera Machine Studying.

The Evolution of AI-Powered Help

At Cloudera, we perceive the challenges confronted by knowledge practitioners. The complexities of recent knowledge workflows usually translate into numerous hours spent coding, debugging, and optimizing fashions. Recognizing this ache level, we got down to redefine the information science expertise with AI-driven innovation.

Cloudera Copilot for Cloudera Machine Studying integrates cutting-edge giant language fashions straight into the machine studying service. This integration empowers builders and knowledge scientists alike with superior capabilities for code completion, era, and troubleshooting. Whether or not you’re tackling knowledge transformation challenges or refining intricate machine studying fashions, our Copilot is designed to be your dependable companion in innovation.

Accelerating Productiveness with AI

The Cloudera Copilot for Cloudera Machine Studying redefines knowledge practitioners’ workflows throughout essential areas: code era and autocompletion, debugging and troubleshooting, and code understanding and exploration. 

In code era and autocompletion, the Copilot dramatically accelerates the event lifecycle. It begins by producing preliminary notebooks to kickstart initiatives and continues to help in writing features, take a look at instances, or documentation. This sensible help accelerates challenge initiation and maintains constant coding practices. By automating these important duties, the Copilot frees up builders’ time to focus on innovation and problem-solving somewhat than spending useful time on repetitive duties. This built-in assistant fosters a extra environment friendly growth course of and boosts total productiveness in knowledge science and machine studying endeavors.

 

 

With regards to debugging and troubleshooting, the Copilot reduces time to resolution by clarifying error messages, figuring out bugs inside code, and proposing sensible options akin to: analyzing static code, recognizing widespread points, and offering proactive suggestions to handle them. This proactive strategy not solely improves code high quality but additionally empowers builders to resolve points extra effectively, fostering a smoother growth course of and minimizing disruptions to challenge timelines.

 

Within the realm of code understanding and exploration, the Cloudera Copilot for Cloudera Machine Studying accelerates developer onboarding and assists in navigating advanced initiatives. By capturing project-specific data, the Copilot can help new group members in greedy challenge buildings, serving to with high-level questions, and explaining implementation particulars. This functionality accelerates studying curves and fosters higher collaboration inside groups of various experience, finally resulting in extra environment friendly growth cycles and enhanced challenge outcomes.

 

 

The Way forward for Knowledge Science, Immediately

As we proceed to innovate inside Cloudera Machine Studying, the introduction of Cloudera Copilot marks a major leap ahead in knowledge science practitioner productiveness capabilities. Our dedication to empowering knowledge practitioners extends past mere automation—it’s about equipping them with the instruments to quickly drive innovation and obtain significant outcomes.

Get Began Immediately

Are you able to unlock the total potential of your knowledge workflows? Discover the capabilities of Cloudera Copilot for Cloudera Machine Studying and expertise firsthand how AI-powered help can rework your productiveness. 

Engineering Quick-Paced Multiplayer Video games with Gabriel Gambetta

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Engineering Quick-Paced Multiplayer Video games with Gabriel Gambetta


Gabriel joins the present to speak about his historical past with sport growth, client-server sport structure, rubber-banding, raytracing, rasterizers, and way more.

Joe Nash is a developer, educator, and award-winning neighborhood builder, who has labored at corporations together with GitHub, Twilio, Unity, and PayPal. Joe acquired his begin in software program growth by creating mods and operating servers for Garry’s Mod, and sport growth stays his favourite technique to expertise and discover new applied sciences and ideas.

This episode of Software program Engineering Day by day is delivered to you by Vantage. Have you learnt what your cloud invoice will probably be for this month?

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Lately, WorkOS introduced the acquisition of Warrant, the Positive Grained Authorization service. Warrant’s product relies on a groundbreaking authorization system known as Zanzibar, which was initially designed by Google to energy Google Docs and YouTube. This permits quick authorization checks at monumental scale whereas sustaining a versatile mannequin that may be tailored to even probably the most advanced use instances.

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Posit AI Weblog: Deep Studying and Scientific Computing with R torch: the e book

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Posit AI Weblog: Deep Studying and Scientific Computing with R torch: the e book


First issues first: The place are you able to get it? As of as we speak, you may obtain the e-book or order a print copy from the writer, CRC Press; the free on-line version is right here. There’s, to my information, no drawback to perusing the web model – moreover one: It doesn’t have the squirrel that’s on the e book cowl.

A red squirrel on a tree, looking attentively.

So for those who’re a lover of fantastic creatures…

What’s within the e book?

Deep Studying and Scientific Computing with R torch has three components.

The primary covers the indispensible fundamentals: tensors, and the right way to manipulate them; automated differentiation, the sine qua non of deep studying; optimization, the technique that drives most of what we name synthetic intelligence; and neural-network modules, torch's method of encapsulating algorithmic movement. The main focus is on understanding the ideas, on how issues “work” – that’s why we do issues like code a neural community from scratch, one thing you’ll most likely by no means do in later use.

Foundations laid, half two – significantly extra sizeable – dives into deep-learning functions. It’s right here that the ecosystem surrounding core torch enters the highlight. First, we see how luz automates and significantly simplifies many programming duties associated to community coaching, efficiency analysis, and prediction. Making use of the wrappers and instrumentation services it gives, we subsequent study two points of deep studying no real-world software can afford to neglect: make fashions generalize to unseen information, and the right way to speed up coaching. Methods we introduce maintain re-appearing all through the use instances we then have a look at: picture classification and segmentation, regression on tabular information, time-series forecasting, and classifying speech utterances. It’s in working with photos and sound that important ecosystem libraries, particularly, torchvision and torchaudio, make their look, for use for domain-dependent performance.

Partially three, we transfer past deep studying, and discover how torch can determine basically mathematical or scientific functions. Distinguished subjects are regression utilizing matrix decompositions, the Discrete Fourier Rework, and the Wavelet Rework. The first purpose right here is to know the underlying concepts, and why they’re so vital. That’s why, right here similar to partly one, we code algorithms from scratch, earlier than introducing the speed-optimized torch equivalents.

Now that you understand in regards to the e book’s content material, it’s possible you’ll be asking:

Who’s it for?

Briefly, Deep Studying and Scientific Computing with R torch – being the one complete textual content, as of this writing, on this matter – addresses a large viewers. The hope is that there’s one thing in it for everybody (properly, most everybody).

If you happen to’ve by no means used torch, nor some other deep-learning framework, beginning proper from the start is the factor to do. No prior information of deep studying is anticipated. The belief is that you understand some fundamental R, and are accustomed to machine-learning phrases resembling supervised vs. unsupervised studying, training-validation-test set, et cetera. Having labored by way of half one, you’ll discover that components two and three – independently – proceed proper from the place you left off.

If, alternatively, you do have fundamental expertise with torch and/or different automatic-differentiation frameworks, and are principally fascinated with utilized deep studying, it’s possible you’ll be inclined to skim half one, and go to half two, testing the functions that curiosity you most (or simply browse, on the lookout for inspiration). The domain-dependent examples have been chosen to be reasonably generic and easy, in order to have the code generalize to an entire vary of comparable functions.

Lastly, if it was the “scientific computing” within the title that caught your consideration, I actually hope that half three has one thing for you! (Because the e book’s writer, I’ll say that penning this half was a particularly satisfying, extremely partaking expertise.) Half three actually is the place it is sensible to speak of “searching” – its subjects hardly rely upon one another, simply go searching for what appeals to you.

To wrap up, then:

What do I get?

Content material-wise, I feel I can contemplate this query answered. If there have been different books on torch with R, I’d most likely stress two issues: First, the already-referred-to concentrate on ideas and understanding. Second, the usefulness of the code examples. By utilizing off-the-shelf datasets, and performing the standard sorts of duties, we write code match to function a begin in your personal functions – offering templates able to copy-paste and adapt to a goal.

Thanks for studying, and I hope you benefit from the e book!

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