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Qualcomm, the world’s biggest supplier of mobile phone chips, said on Tuesday it had named its president and chip division head Cristiano Amon as its new chief executive.

Amon, who has been with the San Diego-based company since 1995 and became president in 2018, will replace outgoing CEO Steven Mollenkopf, effective June 30.

In recent years Amon has overseen the company’s chip division, which supplies processors to most Android phones and modem chips that help Android devices and Apple’s iPhone models connect to wireless data networks.

A strong proponent of 5G, the new generation of faster wireless networks, he has led Qualcomm’s push to put 5G chips into low and mid-priced handsets. He has also guided the company’s expansion into new areas such as 5G infrastructure equipment, automotive computers, and personal computers.

“We have been at the forefront of innovation for decades and I look forward to maintaining this position going forward,” said the 50-year-old, a native of Brazil who restores vintage muscle cars in his spare time.

But Amon, who also played a role in Qualcomm’s licensing division as company president, will face some tough challenges as CEO, such as how to deal with Qualcomm’s heavy reliance on intellectual property from Arm for its processor chips.

Arm is in the middle of a $40 billion  (roughly Rs. 2,93,600 crores) takeover by Nvidia, which has a brewing rivalry with Qualcomm in chips for artificial intelligence.

Amon could be forced to find a replacement for Arm’s intellectual property if Qualcomm concludes that depending on a competitor creates too much long-term risk.

Qualcomm has already started using more of its own intellectual property in chips for artificial intelligence and is using an Arm alternative called RISC-V in certain parts of its phone processors.

Qualcomm designs chips but outsources their manufacturing, largely to Taiwan Semiconductor Manufacturing in Taiwan and Samsung in Korea. US lawmakers recently approved a programme to bolster domestic semiconductor manufacturing. Amon said Qualcomm, which has huge sway with chip factories because of its sales volume, plans to retain its outsourcing strategy but would consider future US factories.

“We’re one of the few companies that actually have multi-sourcing on the leading node. And we expect to be that way,” he told reporters during a press conference. “We actually look very favorably on more foundry investment, including in the United States. That’s very good for Qualcomm and very good for the industry.”

The current CEO, Mollenkopf, is himself no stranger to challenges, having guided Qualcomm through three crises: A hostile takeover attempt by Broadcom, an antitrust challenge by the US Federal Trade Commission and a protracted legal battle with Apple.

Qualcomm prevailed in all three cases, and the 52-year-old, who has been with the company for 26 years, leaves with its shares riding at nearly three times their value during the depth of the crises.

“Steve navigated through unprecedented circumstances during his tenure, facing more in his seven years as CEO than most leaders face in their entire careers,” said Mark McLaughlin, chair of Qualcomm’s board.

Mollenkopf will remain with Qualcomm as an adviser for a period of time, the company said.

© Thomson Reuters 2020


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These Microsoft Azure tools can help you unlock the secrets lurking in your business data

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How to develop business insights from big data using Microsoft’s Azure Synapse and Azure Data Lakes technologies.

Image: Microsoft

Data lakes are an important part of a modern data analysis environment. Instead of importing all your different data sources into one data warehouse, with the complex task of building import pipelines for relational, non-relational and other data, and of trying to normalise all that data against your choice of keys, you wrap all your data in a single storage environment. On top of that storage pool, you can start to use a new generation of query tools to explore and analyse that data, working with what could be petabytes of data in real time. 

SEE: Windows 10 Start menu hacks (TechRepublic Premium)

Using data this way makes it easier to work with rapidly changing data, getting insights quickly and building reporting environments that can flag up issues as they arise. By wrapping data in one environment, you can take advantage of common access control mechanisms, applying role-based authentication and authorisation, ensuring that the right person gets access to the right data, without leaking it to the outside world. 

Working at scale with Azure Data Lake 

Using tools like Azure Active Directory and Azure Data Lake, you can significantly reduce the risk of a breach as it taps into the Microsoft Security Graph, identifying common attack patterns quickly. 

Once your data is in an Auzre Data Lake store, then you can start to run your choice of analytics tooling over it, using tools like Azure Databricks, the open-source HDInsight, or Azure’s Synapse Analytics. Working in the cloud makes sense here, as you can take advantage of large-scale Azure VM instances to build in-memory models as well as taking advantage of scalable storage to build elastic storage pools for your data lake contents. 

Microsoft recently released a second generation of Data Lake Storage, building on Azure Blobs to add disaster recovery and tiered storage to help you manage and optimise your storage costs. Azure Data Lake Storage is designed to work with gigabits of data throughput. A hierarchical namespace makes working with data easier, using directories to manage your data. And as you’re still using a data lake with many different types of data, there’s still no need for expensive and slow ETL-based transformations. 

Analysing data in Azure Synapse 

Normally you need separate analytics tooling for different types of data. If you’re building tooling to work with your own data lake, you’re often bringing together data-warehousing applications alongside big data tools, resulting in complex and often convoluted query pipelines that can be hard to document and debug. Any change in the underlying data model can be catastrophic, thanks to fragile custom analysis environments. 

Azure now offers an alternative, hybrid analytical environment in the shape of Azure Synapse Analytics, which brings together big data tooling and relational queries in a single environment by mixing SQL with Apache Spark and providing direct connections to Azure data services and to the Power Platform. It’s a combination that allows you to work at global scale while still supporting end-user visualisations and reports, and at the same time providing a platform that supports machine-learning techniques to add support for predictive analytics. 

At its heart, Synapse removes the usual barriers between standard SQL queries and big data platforms, using common metadata to work with both its own SQL dialect and Apache Spark on the same data sets, either relational tables or other stores, including CSV and JSON. It has its own import tooling that will import data into and out of data lakes, with a web-based development environment for building and exploring analytical models that go straight from data to visualisations. 

Synapse creates a data lake as part of its setup, by default using a second-generation BLOB-based instance. This hosts your data containers, in a hierarchical virtual file system. Once the data lake and associated Synapse workspace are in place, you can use the Azure Portal to open the Synapse Studio web-based development environment. 

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Writing a PySpark query in a Spark (Scala) notebook in Azure Synapse Studio.

Image: Microsoft

Building analytical queries in Synapse Studio 

Synapse Studio is the heart of Azure Synapse Analytics, where data engineers can build and test models before deploying them in production. SQL pools manage connections to your data, using either serverless or dedicated connections. While developing models, it’s best to use the built-in serverless pool; once you’re ready to go live you can provision a dedicated pool of SQL resources that can be scaled up and down as needed. However, it’s important to remember that you’re paying for those resources even if they’re not in use. You can also set up serverless pools for Apache Spark, helping keep costs to a minimum for hybrid queries. There is some overhead when launching serverless instances, but for building reports as a batch process, that shouldn’t be an issue. 

Azure Synapse is fast: building a two-million row table takes just seconds. You can quickly work with any tabular data using familiar SQL queries, using the Studio UI to display results as charts where necessary. That same data can be loaded from your SQL store into Spark, without writing any ETL code for data conversion. All you need to do is create a new Spark notebook, and then create the database and import it from your SQL pool. Data from Spark can be passed back to the SQL pool; allowing you to use Spark to manipulate data sets for further analysis. You can use SQL queries on Spark datasets directly, simplifying what could otherwise be complex programming tasks unifying results from different platforms. 

SEE: Checklist: Securing Windows 10 systems (TechRepublic Premium)

One useful feature of Azure Data Lakes using Gen 2 storage is the ability to link to other storage accounts, allowing you to quickly work with other data sources without having to import them into your data lake store. Using Azure Synapse Studio, your queries are stored in notebooks. These notebooks can be added to pipelines to automate analysis. You can set triggers to run an analysis at set intervals, driving Power BI-based dashboards and reports. 

There’s a lot to explore with Synapse Studio, and to get the most from it requires plenty of data-engineering experience. It’s not a tool for beginners or for end users: you need to be experienced in both SQL-based data-warehousing techniques and in tools like Apache Spark. However, it’s the combination of those tools and the ability to publish results in desktop analytical tools like Power BI that makes it most useful. 

The cost of at-scale data lake analysis will always make it impossible to bring to everyone. But using a single environment to create and share analyses should go a long way towards unlocking the utility of business data. 

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Moonshots for the Treatment of Aging: Less Incrementalism, More Ambition

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There is far too much incrementalism in the present research and development of therapies to treat aging. Much of the field is engaged in mimicking calorie restriction or repurposing existing drugs that were found to increase mouse life span by a few percentage points. This will not meaningfully change the shape of human life, but nonetheless costs just as much as efforts to achieve far more.

If billions of dollars and the efforts of thousands of researchers are to be devoted to initiatives to treat aging, then why not pursue the ambitious goal of rejuvenation and adding decades to healthy life spans? It is just as plausible.

Moonshots for the Treatment of Aging Less Incrementalism More Ambition

Image credit: Pixabay (Free Pixabay license)

There are just as many starting points and plausible research programs aimed at outright rejuvenation via repair of molecular damage, such as those listed in the SENS approach to aging, as there are aimed at achieving only small benefits in an aged metabolism. The heavy focus on incremental, low yield programs of research and development in the present community is frustrating, and that frustration is felt by many.

As the global population ages, there is increased interest in living longer and improving one’s quality of life in later years. However, studying aging – the decline in body function – is expensive and time-consuming. And despite research success to make model organisms live longer, there still aren’t really any feasible solutions for delaying aging in humans. With space travel, scientists and engineers couldn’t know what it would take to get to the moon. They had to extrapolate from theory and shorter-range tests. Perhaps with aging, we need a similar moonshot philosophy. Like the moon once was, we seem a long way away from provable therapies to increase human healthspan or lifespan. This review therefore focuses on radical proposals. We hope it might stimulate discussion on what we might consider doing significantly differently than ongoing aging research.

A less than encouraging sign for many of the lifespan experiments done in preclinical models, namely in mammals such as mice, is that they have modest effect sizes, often only having statistically significant effects in one of the genders, and often only in specific dietary or housing conditions. Even inhibiting one of the most potent and well-validated aging pathways, the mechanistic target of rapamycin (mTOR) pathway has arguably modest effects on lifespan – a 12-24% increase in mice. This is all to ask, if the mTOR inhibitor rapamycin is one of the potential best-case scenarios and might be predicted to have a modest effect if any (and possibly a detrimental one) in people, should it continue to receive so much focus by the aging community? Note the problems in the aging field with small and inconsistent effects for the leading strategies aren’t specific to rapamycin.

Treating individual aging-related diseases has encountered roadblocks that should also call into question whether we are on the optimal path for human aging. Alzheimer’s is a particularly well-funded and well-researched aging-related topic where there are still huge gaps in our understanding and lack of good treatment options. There has been considerable focus on amyloid beta and tau, but targeting those molecules hasn’t done much for Alzheimer’s so far, leaving many searching for answers. The point is when we spend collectively a long time on something that isn’t working well, such as manipulating a single gene or biological process, it should seem natural to consider conceptually different approaches.

Link: https://doi.org/10.3233/NHA-190064

Source: Fight Aging!




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Signal Back Up: Users May See Some Errors, Company Says Will Be Fixed in Next Update

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Signal said it had restored its services a day after the application faced technical difficulties as it dealt with a flood of new users after rival messaging app WhatsApp announced a controversial change in privacy terms.

Signal has seen a rise in downloads following a change in WhatsApp’s privacy terms, that required WhatsApp users to share their data with both Facebook and Instagram.

Signal users might see errors in some chats as a side effect to the outage, but will be resolved in the next update of the app, the company said in a tweet.

The error does not affect the security of the chat, the company added.

The non-profit Signal Foundation based in Silicon Valley, which currently oversees the app, was launched in February 2018 with Brian Acton, who co-founded WhatsApp before selling it to Facebook, providing initial funding of $50 million (roughly Rs. 365 crores).

Signal faced a global outage that began on January 15. Although users could open the app and send messages, nothing was actually delivered.

Signal later sent Gadgets 360 a message with the following statement from its COO Aruna Harder: “We have been adding new servers and extra capacity at a record pace every single day this week, but today exceeded even our most optimistic projections. Millions upon millions of new users are sending a message that privacy matters, and we are working hard to restore service for them as quickly as possible.”

© Thomson Reuters 2021


Does WhatsApp’s new privacy policy spell the end for your privacy? We discussed this on Orbital, our weekly technology podcast, which you can subscribe to via Apple Podcasts, Google Podcasts, or RSS, download the episode, or just hit the play button below.



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