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HBCU college students chart their profession paths in America’s Music Metropolis

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How we fought unhealthy apps and unhealthy actors in 2023


A protected and trusted Google Play expertise is our prime precedence. We leverage our SAFE (see under) ideas to supply the framework to create that have for each customers and builders. Here is what these ideas imply in observe:

  • (S)afeguard our Customers. Assist them uncover high quality apps that they’ll belief.
  • (A)dvocate for Developer Safety. Construct platform safeguards to allow builders to give attention to development.
  • (F)oster Accountable Innovation. Thoughtfully unlock worth for all with out compromising on person security.
  • (E)volve Platform Defenses. Keep forward of rising threats by evolving our insurance policies, instruments and know-how.

With these ideas in thoughts, we’ve made latest enhancements and launched new measures to proceed to maintain Google Play’s customers protected, even because the menace panorama continues to evolve. In 2023, we prevented 2.28 million policy-violating apps from being revealed on Google Play1 partly due to our funding in new and improved security measures, coverage updates, and superior machine studying and app evaluate processes. Now we have additionally strengthened our developer onboarding and evaluate processes, requiring extra id data when builders first set up their Play accounts. Along with investments in our evaluate tooling and processes, we recognized unhealthy actors and fraud rings extra successfully and banned 333K unhealthy accounts from Play for violations like confirmed malware and repeated extreme coverage violations.

Moreover, nearly 200K app submissions have been rejected or remediated to make sure correct use of delicate permissions reminiscent of background location or SMS entry. To assist safeguard person privateness at scale, we partnered with SDK suppliers to restrict delicate information entry and sharing, enhancing the privateness posture for over 31 SDKs impacting 790K+ apps. We additionally considerably expanded the Google Play SDK Index, which now covers the SDKs utilized in nearly 6 million apps throughout the Android ecosystem. This priceless useful resource helps builders make higher SDK decisions, boosts app high quality and minimizes integration dangers.

Defending the Android Ecosystem

Constructing on our success with the App Protection Alliance (ADA), we partnered with Microsoft and Meta as steering committee members within the newly restructured ADA below the Joint Improvement Basis, a part of the Linux Basis household. The Alliance will help industry-wide adoption of app safety finest practices and tips, in addition to countermeasures towards rising safety dangers.

Moreover, we introduced new Play Retailer transparency labeling to spotlight VPN apps which have accomplished an unbiased safety evaluate by App Protection Alliance’s Cellular App Safety Evaluation (MASA). When a person searches for VPN apps, they may now see a banner on the prime of Google Play that educates them in regards to the “Impartial safety evaluate” badge within the Information security part. This helps customers see at-a-glance {that a} developer has prioritized safety and privateness finest practices and is dedicated to person security.

To raised defend our clients who set up apps outdoors of the Play Retailer, we made Google Play Shield’s safety capabilities much more highly effective with real-time scanning on the code-level to fight novel malicious apps. Our safety protections and machine studying algorithms study from every app submitted to Google for evaluate and we take a look at hundreds of alerts and evaluate app conduct. This new functionality has already detected over 5 million new, malicious off-Play apps, which helps defend Android customers worldwide.

Extra Stringent Developer Necessities and Tips

Final 12 months we up to date Play insurance policies round Generative AI apps, disruptive notifications, and expanded privateness protections. We are also elevating the bar for brand new private developer accounts by requiring new testing necessities earlier than builders could make their app accessible on Google Play. By testing their apps, getting suggestions and guaranteeing all the pieces is prepared earlier than they launch, builders are capable of carry extra prime quality content material to Play customers. As a way to enhance belief and transparency, we’ve launched expanded developer verification necessities, together with D-U-N-S numbers for organizations and a brand new “Concerning the developer” part.

To provide customers extra management over their private information, apps that allow account creation now want to supply an choice to provoke account and information deletion from inside the app and on-line. This net requirement is particularly vital so {that a} person can request account and information deletion with out having to reinstall an app. To simplify the person expertise, we’ve got additionally included this as a function inside the Information security part of the Play Retailer.

With every iteration of the Android working system (together with its strong set of APIs), a myriad of enhancements are launched, aiming to raise the person expertise, bolster safety protocols, and optimize the general efficiency of the Android platform. To additional safeguard our clients, roughly 1.5 million functions that don’t goal the newest APIs are now not accessible within the Play Retailer to new customers who’ve up to date their units to the newest Android model.

Wanting Forward

Defending customers and builders on Google Play is paramount and ever-evolving. We’re launching new safety initiatives in 2024, together with eradicating apps from Play that aren’t clear about their privateness practices.

We additionally not too long ago filed a lawsuit in federal court docket towards two fraudsters who made a number of misrepresentations to add fraudulent funding and crypto trade apps on Play to rip-off customers. This lawsuit is a vital step in holding these unhealthy actors accountable and sending a transparent message that we are going to aggressively pursue those that search to reap the benefits of our customers.

We’re always engaged on new methods to guard your expertise on Google Play and throughout the whole Android ecosystem, and we stay up for sharing extra.

Notes

Introducing Cloudera’s AI Assistants – Cloudera Weblog

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Within the final couple of years, AI has launched itself to the forefront of expertise initiatives throughout industries. In truth, Gartner predicts the AI software program market will develop from $124 billion in 2022 to $297 billion in 2027. As an information platform firm, Cloudera has two very clear priorities. First, we have to assist prospects get AI fashions primarily based on trusted knowledge into manufacturing quicker than ever. And second, we have to construct AI capabilities into Cloudera to present extra folks entry to data-driven insights for his or her on a regular basis roles.

At our current Cloudera Now digital occasion, we introduced three new capabilities that assist each of our AI priorities: An AI-driven SQL assistant, a Enterprise Intelligence (BI) chatbot that converses together with your knowledge, and an ML copilot that accelerates machine studying improvement. Let’s take a deeper dive into how these capabilities speed up your AI initiatives and assist knowledge democratization.

SQL AI Assistant: Your New Finest Buddy

Writing advanced SQL queries could be a actual problem. From discovering the fitting tables and columns to coping with joins, unions, and subselects, then optimizing for readability and efficiency, and doing all of that whereas taking into consideration the distinctive SQL dialect of the engine, it’s sufficient to make even probably the most seasoned SQL developer’s head spin.  And on the finish of the day, not everybody who wants knowledge to achieve success of their day-to-day work is an SQL professional.

Think about, as a substitute, having a site professional and a SQL guru at all times by your aspect. That’s precisely what Cloudera’s SQL AI assistant is. Customers merely describe what they want in plain language, and the assistant will discover the related knowledge, write the question, optimize it, and even clarify it again in easy-to-understand phrases. 

 

 

Underneath the hood, the assistant makes use of superior methods like immediate engineering and retrieval augmented technology (RAG) to actually perceive your database. It really works with many massive language fashions (LLMs), whether or not they’re public or non-public, and it effortlessly scales to deal with 1000’s of tables and customers concurrently. So whether or not you’re beneath strain to reply vital enterprise questions or simply uninterested in wrestling with SQL syntax, the AI assistant has your again, enabling you to give attention to what actually issues – getting insights out of your knowledge.

AI Chatbot in Cloudera Knowledge Visualization: Your Knowledge’s New Finest Buddy

BI dashboards are undeniably helpful, however they usually solely inform a part of the story. To realize significant and actionable insights, knowledge customers want to have interaction in a dialog with their knowledge, and ask questions past merely the “what” {that a} dashboard usually exhibits. That’s the place the AI Chatbot in Cloudera Knowledge Visualization comes into play.

 

The chatbot resides straight inside your dashboard, able to reply any query you pose. And once we say “any query,” we imply it. Why are gross sales down within the Northeast? Will this pattern proceed? What actions ought to we take? The chatbot leverages the context of the information behind the dashboard to ship deeper, extra actionable insights to the consumer. 

A written reply is an effective way to begin understanding your knowledge, however let’s not overlook the facility of the visuals in our dashboards and experiences.  The chatbot eliminates the burden of clicking by dropdowns and filters to search out solutions. Merely ask what you need to know, in plain language, and the chatbot will intelligently match it to the related knowledge and visuals. It’s like having a devoted material professional proper there with you, able to dive deep into the insights that matter most to your enterprise.

Cloudera Copilot for Cloudera Machine Studying: Your Mannequin’s New Finest Buddy

Constructing machine studying fashions is not any simple feat. From knowledge wrangling to coding, mannequin tuning to deployment, it’s a posh and time-consuming course of. In truth, many fashions by no means make it into manufacturing in any respect. However what for those who had a copilot to assist navigate all the challenges associated to deploying fashions in manufacturing?

 

Cloudera’s ML copilots, powered by pre-trained LLMs, are like having machine studying specialists on name 24/7. They’ll write and debug Python code, counsel enhancements, and even generate whole functions from scratch. With seamless integration to over 130 Hugging Face fashions and datasets, you have got a wealth of sources at your disposal.

Whether or not you’re an information scientist trying to streamline your workflow or a enterprise consumer desperate to get an AI utility up and operating rapidly, the ML copilots assist the end-to-end improvement course of and get fashions into manufacturing quick.

Elevate Your Knowledge with AI Assistants

By embedding AI assistants for SQL, BI, and ML straight into the platform, Cloudera is simplifying and enhancing the information expertise for each single consumer. SQL builders shall be extra environment friendly and productive than ever. Enterprise analysts shall be empowered to have significant, actionable conversations with knowledge, uncovering the “why” behind the “what.” Moreover, knowledge scientists shall be empowered to deliver new AI functions to manufacturing quicker and with higher confidence.

For extra data on these options and our AI capabilities, go to our Enterprise AI web page. If you’re prepared, you possibly can request a demo on the backside of the web page to see how these capabilities can work within the context of your enterprise.  

Occasion Interception


By the point adjustments have made their method to the legacy database, then you can argue that it’s too late for
occasion interception.
That mentioned, “Pre-commit” triggers can be utilized to intercept a database write occasion and take completely different actions.
For instance a row could possibly be inserted right into a separate Occasions desk to be learn/processed by a brand new part –
while continuing with the write as earlier than (or aborting it).
Observe that important care needs to be taken in the event you change the present write behaviour as chances are you’ll be breaking
an important implicit contract.

Case Research: Incremental area extraction

Considered one of our groups was working for a consumer whose legacy system had stability points and had turn out to be tough to keep up and gradual to replace.

The organisation was seeking to treatment this, and it had been determined that essentially the most acceptable approach ahead for them was to displace the legacy system with capabilities realised by a Service Primarily based Structure.

The technique that the workforce adopted was to make use of the Strangler Fig sample and extract domains, one after the other, till there was little to not one of the unique software left.
Different concerns that had been in play included:

  • The necessity to proceed to make use of the legacy system with out interruption
  • The necessity to proceed to permit upkeep and enhancement to the legacy system (although minimising adjustments to domains being extracted was allowed)
  • Modifications to the legacy software had been to be minimised – there was an acute scarcity of retained data of the legacy system

Legacy state

The diagram beneath exhibits the structure of the legacy
structure. The monolithic system’s
structure was primarily Presentation-Area-Knowledge Layers.

Occasion Interception

Stage 1 – Darkish launch service(s) for a single area

Firstly the workforce created a set of companies for a single enterprise area together with the potential for the information
uncovered by these companies to remain in sync with the legacy system.

The companies used Darkish Launching – i.e. not utilized by any shoppers, as an alternative the companies allowed the workforce to
validate that knowledge migration and synchronisation achieved 100% parity with the legacy datastore.
The place there have been points with reconciliation checks, the workforce might motive about, and repair them guaranteeing
consistency was achieved – with out enterprise affect.

The migration of historic knowledge was achieved by way of a “single shot” knowledge migration course of. While not strictly Occasion Interception, the continuing
synchronisation was achieved utilizing a Change Knowledge Seize (CDC) course of.

Stage 2 – Intercept all reads and redirect to the brand new service(s)

For stage 2 the workforce up to date the legacy Persistence Layer to intercept and redirect all of the learn operations (for this area) to
retrieve the information from the brand new area service(s). Write operations nonetheless utilised the legacy knowledge retailer. That is
and instance of Department by Abstraction – the interface of the Persistence Layer stays unchanged and a brand new underlying implementation
put in place.

Stage 3 – Intercept all writes and redirect to the brand new service(s)

At stage 3 various adjustments occurred. Write operations (for the area) had been intercepted and redirected to create/replace/take away
knowledge throughout the new area service(s).

This variation made the brand new area service the System of Report for this knowledge, because the legacy knowledge retailer was now not up to date.
Any downstream utilization of that knowledge, equivalent to reviews, additionally needed to be migrated to turn out to be a part of or use the brand new
area service.

Stage 4 – Migrate area enterprise guidelines / logic to the brand new service(s)

At stage 4 enterprise logic was migrated into the brand new area companies (remodeling them from anemic “knowledge companies”
into true enterprise companies). The entrance finish remained unchanged, and was now utilizing a legacy facade which
redirected implementation to the brand new area service(s).

Contrastive Studying from AI Revisions (CLAIR): A Novel Strategy to Deal with Underspecification in AI Mannequin Alignment with Anchored Choice Optimization (APO)


Synthetic intelligence (AI) growth, notably in massive language fashions (LLMs), focuses on aligning these fashions with human preferences to reinforce their effectiveness and security. This alignment is crucial in refining AI interactions with customers, making certain that the responses generated are correct and aligned with human expectations and values. Attaining this requires a mix of desire knowledge, which informs the mannequin of fascinating outcomes, and alignment aims that information the coaching course of. These parts are essential for bettering the mannequin’s efficiency and skill to satisfy consumer expectations.

A big problem in AI mannequin alignment lies within the situation of underspecification, the place the connection between desire knowledge and coaching aims isn’t clearly outlined. This lack of readability can result in suboptimal efficiency, because the mannequin could need assistance to study successfully from the offered knowledge. Underspecification happens when desire pairs used to coach the mannequin comprise irrelevant variations to the specified final result. These spurious variations complicate the educational course of, making it tough for the mannequin to deal with the elements that really matter. Present alignment strategies usually have to account extra adequately for the connection between the mannequin’s efficiency and the desire knowledge, doubtlessly resulting in a degradation within the mannequin’s capabilities.

Current strategies for aligning LLMs, resembling these counting on contrastive studying aims and desire pair datasets, have made important strides however have to be revised. These strategies usually contain producing two outputs from the mannequin and utilizing a choose, one other AI mannequin, or a human to pick out the popular output. Nonetheless, this strategy can result in inconsistent desire alerts, as the standards for selecting the popular response would possibly solely typically be clear or constant. This inconsistency within the studying sign can hinder the mannequin’s capability to enhance successfully throughout coaching, because the mannequin could solely typically obtain clear steering on adjusting its outputs to align higher with human preferences.

Researchers from Ghent College – imec, Stanford College, and Contextual AI have launched two modern strategies to handle these challenges: Contrastive Studying from AI Revisions (CLAIR) and Anchored Choice Optimization (APO). CLAIR is a novel data-creation technique designed to generate minimally contrasting desire pairs by barely revising a mannequin’s output to create a most popular response. This technique ensures that the distinction between the successful and shedding outputs is minimal however significant, offering a extra exact studying sign for the mannequin. Then again, APO is a household of alignment aims that provide higher management over the coaching course of. By explicitly accounting for the connection between the mannequin and the desire knowledge, APO ensures that the alignment course of is extra secure and efficient.

The CLAIR technique operates by first producing a shedding output from the goal mannequin, then utilizing a stronger mannequin, resembling GPT-4-turbo, to revise this output right into a successful one. This revision course of is designed to make solely minimal adjustments, making certain that the distinction between the 2 outputs is concentrated on probably the most related elements. This strategy differs considerably from conventional strategies, which could depend on a choose to pick out the popular output from two independently generated responses. By creating desire pairs with minimal but significant contrasts, CLAIR gives a clearer and simpler studying sign for the mannequin throughout coaching.

Anchored Choice Optimization (APO) enhances CLAIR by providing fine-grained management over the alignment course of. APO adjusts the probability of successful or shedding outputs based mostly on the mannequin’s efficiency relative to the desire knowledge. For instance, the APO-zero variant will increase the chance of successful outputs whereas lowering the probability of shedding ones, which is especially helpful when the mannequin’s outputs are typically much less fascinating than the successful outputs. Conversely, APO-down decreases the probability of successful and shedding outputs, which could be useful when the mannequin’s outputs are already higher than the popular responses. This degree of management permits researchers to tailor the alignment course of extra carefully to the precise wants of the mannequin and the information.

The effectiveness of CLAIR and APO was demonstrated by aligning the Llama-3-8B-Instruct mannequin utilizing a wide range of datasets and alignment aims. The outcomes have been important: CLAIR, mixed with the APO-zero goal, led to a 7.65% enchancment in efficiency on the MixEval-Arduous benchmark, which measures mannequin accuracy throughout a spread of advanced queries. This enchancment represents a considerable step in direction of closing the efficiency hole between Llama-3-8B-Instruct and GPT-4-turbo, decreasing the distinction by 45%. These outcomes spotlight the significance of minimally contrasting desire pairs and tailor-made alignment aims in bettering AI mannequin efficiency.

In conclusion, CLAIR and APO supply a simpler strategy to aligning LLMs with human preferences, addressing the challenges of underspecification and offering extra exact management over the coaching course of. Their success in bettering the efficiency of the Llama-3-8B-Instruct mannequin underscores their potential to reinforce the alignment course of for AI fashions extra broadly.


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Asif Razzaq is the CEO of Marktechpost Media Inc.. As a visionary entrepreneur and engineer, Asif is dedicated to harnessing the potential of Synthetic Intelligence for social good. His most up-to-date endeavor is the launch of an Synthetic Intelligence Media Platform, Marktechpost, which stands out for its in-depth protection of machine studying and deep studying information that’s each technically sound and simply comprehensible by a large viewers. The platform boasts of over 2 million month-to-month views, illustrating its reputation amongst audiences.