Monthly Archives: June 2022

AWS DeepRacer Student League kicks off today; the competition provides an exciting opportunity for students across India to learn and experiment with machine learning

The Ministry of Education is working with Amazon Web Services (AWS) with an aim to impart cloud computing and machine learning (ML) skills to higher education students in India. A Memorandum of Understanding (MoU) was signed between the All India Council for Technical Education (AICTE),a statutory body under the Ministry, and Amazon Internet Services Private Limited (AISPL), which undertakes the resale and marketing of AWS in India. This new collaboration extends the Ministrys efforts to enable students with critical technology skills, and strengthen the focus to build a future-ready digitally-skilled workforce in India. Thousands of AICTE-affiliated colleges in the country will extend this initiative to benefit students.

Dr. Buddha Chandrashekhar, Chief Coordinating Officer, AICTE, said, Digital skilling on future technologies at a national scale is a key priority for the Ministry of Education. Skilling our students in cloud computing and machine learning is especially crucial to not only ensure employability for our students, but also to build capacity in these critical skills that will define the industries of tomorrow. We are pleased that an industry leader like AWS is committed to work with AICTE to support the Indian governments vision for skilling to create an Atmanirbhar Bharat.

Sunil PP, LeadEducation, Space, and Nonprofits, Amazon Internet Services Private Limited, AWS India and South Asia, said, Over the course of the pandemic, we have seen organizations of all sizes accelerate their digital transformation plans by several years, driving an increased need for employers and their workers to advance skills training for cloud computing, cybersecurity, and machine learning.AWS recognizes this as a national priority, and todays announcement of the MoU with the Ministry of Education is part of our continued commitment to support the government in developing Indias technology talent and strengthening the countrys digital economy.

Through the MoU, AWS will provide students access to AWS Educate (www.awseducate.com), a program that offers self-paced online cloud learning resources and labs, designed to help people learn, practice, and evaluate cloud skills. The programseasy-to-navigate and adaptive user experience guides learners totargeted training content based on their knowledge, goals and interests.Learners can register on AWS Educate with just an email address.

In addition, AWS will support the goal of skilling students in cloud computing and ML through theAWS DeepRacer Student League, launched today. The AWS DeepRacer Student League isaimed at introducing ML to students in higher education, and inspiring them to explore the technology. The competition provides participants an interesting and fun way to learn ML, and experiment with it by building autonomous driving applications.This is the second AWS DeepRacer initiative focused on students in India after theAWS DeepRacer Womens Leaguein 2021, and is being held with support from the Ministry of Education, AICTE, and Intel. The competition is open to students above 18 years who are currently enrolled in higher educational institutions in India.

Earlier this year, the research report Building Digital Skills for the Changing Workforce prepared by strategy and economics consulting firm AlphaBeta, and commissioned by AWS noted that four of the top six most in-demand skills in India by 2025 will be cloud-related. The report found that the ability to use cloud-based tools will be the most in-demand skill required by employers in India by 2025, followed by technical support skills, cybersecurity skills, advanced digital marketing, artificial intelligence (AI) andML, and cloud architecture design. The importance and impact of the cloud-related skills are further underscored by their wide use across industry sectors, as employers surveyed in the research expect these skills to be in high demand in sectors such as healthcare, agriculture, fintech, media, and entertainment.

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Ministry of Education, AICTE and AWS to Upskill Students in India in Cloud Computing and Machine Learning - CXOToday.com

Army CIO Dr. Raj Iyer speaks at the Pentagons Hall of Heroes during a pinning ceremony on Dec. 15, 2020. (DVIDS)

WASHINGTON: The Armys chief information officer said he wants fiscal 2023 to be the services year of inflection for digital transformation, as he revealed details about how the service plans to spend its requested $16.6 billion in cyber and IT funding.

Its very, very tempting to continue to spend money on technologies that are 10 years old because weve gotten comfortable with them, CIO Raj Iyer said Thursday during a briefing with reporters. We know that they work to meet todays needs and its so much easier to just keep them onThats not whats going to help us fight and winfor the Army of 2030.

Iyer said that the overall budget is almost flat going from FY22 to FY23, so the service needs to closely watch how it uses its money and continuously reprioritize its budget in support of future modernization.

Of the $16.6 billion for cyber and IT next year, he said $2 billion would be allocated directly to the cyber piece of the funding for offensive and defensive operations, and research and development in cybersecurity. The bulk of the investment, about $9.8 billion, will go towards the Army network, and $220 million will go towards all things AI.

Network modernization is led by the Armys Network Cross-Functional Team and Program Executive Office-Command, Control, Communications-Tactical teams, which are developing and fielding capability sets tool kits of applications and technology designed to modernize network capabilities across the service every two years.

The first set, Capability Set 21, is already about 70% fielded and elements of the set are being used by units deployed in Europe to support NATO. The set is focused on making the network more intuitive and includes single-channel commercial radios with advanced networking waveforms, among other components designed to enable resilient communication.

RELATED: Ukraine Invasion Reinforcing Armys Work On Secure Networks, Comms

The Army this month is anticipated to conduct a two-phased operational assessment with its next set, Capability Set 23, both with the 2nd Cavalry Regiment in Europe to get real time operational feedback to help the service decide what to field.

Later this year, the service plans to begin experimenting with US Army Pacific Command for cloud, data and mission command applications while, at the same time, developing capability sets 25 and 27.

The network funding is clearly supporting all the way from the tactical edge, including support to current operations, all the way to investments were making in the cloud, Iyer said. [FY]23 really is also our opportunity to scale our efforts that we have made some tremendous progress [with] in [FY] 21 or 22. And so were seeing about a $290 million investment in cloud in FY23 to continue to further our cloud migration journey.

The service is expected to release its updated cloud plan later this year, Paul Puckett, the director of the Armys Enterprise Cloud Management Office, said in May.

What youre going to see is a greater level of detail and maturity for not just leveraging commercial cloud computing, but really how that starts to extend into our on-premise locations, how this starts to extend into our tactical locations and how we start to kind of poke at some of the mission-enabling capabilities of how the Army operates, enabled by cloud computing, Puckett said May 24 at an Amazon Web Services conference.

On the research and development side, the Army wants to invest around quantum computing and supercomputing requirements. Quantum science has become an area of focus for both DoD and the White House.

In May, President Joe Biden signed two directives aimed at advancing quantum science, including a memorandum outlining his administrations plan to address national security risks posed by quantum computers that could be capable of cracking DoDs encryption.

The Army in FY23 also wants to invest $1.4 billion to modernize its legacy enterprise resource planning systems responsible for the services financial, training, logistics and human resources-related activities.

Its unclear if that funding is part of the larger $16.6 billion cyber and IT funding Iyer laid out, but he said the Army will award multiple OTAs to initiate prototypes that will run anywhere from 12 to 18 months for enterprise business system conversations and award a production contract once it down selects a prototype.

Some of the things that we will be looking for as part ofthis prototype is to look at how modular the architecture is, again, to make sure that is future-proofed, he said. Well be looking at the ability to support data exchange through APIs and micro-services Well be looking at the system being cloud native from the get-go and making sure that we can fully benefit from a future modern architectureWell be looking at how flexible the solution will be in terms of its ability to implement Army-unique processes where we have them without the need to customize commercial-off-the-shelf products.

The rest is here:

Army CIO: FY23 is 'year of inflection' for digital transformation - Breaking Defense

Enterprises are in the early phases of a revolution whose mission is to make more kinds of business systems, equipment and processes perform with less human intervention. This autonomous revolution is rapidly moving from one-off experiments to a collective effort to build digital fabrics that can keep up with the rapid pace of change in supply chains, geopolitics and the environment. The promise? An increase in efficiency, scalability and profitability on a level previously unknown.

The effort is already shaping enterprise functions. Autonomous IT systems are patching security vulnerabilities, autonomous chatbots are managing customer experience and a new generation of robots is automating the warehouse. The use of AI to improve IT infrastructure has become a quest to develop an autonomous infrastructure that is infused with sophisticated decision engines and spans the enterprise.

"I've always felt that the vision should be to open up an automation platform and say, 'Automate my enterprise!'" said Craig Stewart, chief data officer at software company SnapLogic, which specializes in infrastructure-as-a-service tools.

In Stewart's vision, the platform would be able to understand the applications and data in use, the automation required and the next best action to take. Granted, there's a way to go for this to happen, he said, but reality is getting closer.

"As the journey progresses, the user is getting more accurate predictions and getting them more rapidly on the automations required," he said.

Progress is being driven by a convergence of cloud, data and IoT infrastructures. As these technologies become more connected, they are stitching pockets of autonomous systems into a more capable and coherent whole. The result is that physical spaces, business processes and IT are being transformed across industries and across enterprise departments:

"We have come to a stage where autonomous systems are interdependent and can no longer be considered in isolation," said Jaikumar Ganesh, head of cloud engineering at Anyscale, creators of Ray, an open source framework in distributed machine learning. "In addition, they continue to become smarter with feedback loops to take on more complex tasks."

Ganesh previously worked at Uber, where he led a program that uses historical and real-time location data along with a machine learning model to continuously improve ETA prediction. The model is regularly updated as more data is gathered. Uber's automated system for ETA prediction in turn has an impact on the service's supply and demand marketplace, which is another automated system. To understand the consequences of these overlapping systems working in tandem, Uber needs to think about them as a whole and study their interactions.

"Thinking of them as a network, understanding the interdependencies and bottlenecks, is crucial to making them work well," Ganesh said.

Similarly, in a physical warehouse, robots on one part of the assembly line need to be in sync and in coordination with robots on another part of the assembly line. The entire system needs to be thought of as a collection of mini automated systems, Ganesh explained, adding that physical infrastructure will need to be rethought to handle these overlapping autonomous systems. Indeed, new roadway designs for autonomous cars and warehouses designed explicitly for robots are already in the works.

Autonomous systems certainly are not new. Computer scientists have been exploring the core ideas of autonomous systems in different contexts for decades.

In 2001, IBM and academic researchers published the MAPE (Monitor-Analyze-Plan-Execute) architectural reference model for autonomic computing. It characterized autonomous systems as a "computer system capable of sensing environments, interpreting policy, accessing knowledge (data, information, knowledge), making decisions, and initiating dynamically assembled routines of choreographed activity to both complete a process and update the set of environmental variables that enables the autonomic system to self-manage its own operation and the processes it oversees."

A few years later, autonomous computing entered the public realm when, in 2004, the Defense Advanced Research Projects Agency held its first grand challenge: a driverless car race across the Mojave Desert.

"Autonomy, autonomous and autonomic are big concepts that have been the penultimate goal of systems for at least the last 20 years," said Jon Theuerkauf, chief customer strategy and transformation officer at Blue Prism, an RPA vendor.

During the intervening years, researchers, engineers and IT executives have explored ways to strike the right balance between greater autonomy and safety. "It's [satisfying] the demands of complexity and risk prevention at an economical price that are the most challenging for autonomous robotics," Theuerkauf said.

Let's explore how vendors and companies are meeting these challenges as the use of autonomous systems in physical spaces, business processes and IT continues to evolve. First up: robotics.

Fixed robots have been around for decades in industries such as auto manufacturing. But these simple brutes were locked behind enclosures to protect workers and other equipment from accidents.

A new generation of more intelligent, more autonomous and more collaborative robots are emerging from their cages into warehouses worldwide. Interact Analysis predicted that the mobile robot population could grow from 9,000 in 2020 to 53,000 by 2025. In addition, the firm expects a total of 2.1 million robots of all types by 2025, with about 860,000 introduced in 2025 alone.

Early implementations of mobile robots have focused on improving existing warehouse automation efforts with function-specific robots for moving boxes, unloading trucks, fetching products from shelves and packing boxes. Some firms like AutoStore and Ocado Engineering have begun to apply engineered automation, which turns whole facilities into coherent autonomous ensembles.

Outside the warehouse, robots are starting to take jobs on farms, construction sites, mines and surveillance firms. Service robots autonomously cut lawns and clean hallways. One Accenture report observed that mining operators are seeing an average 15% productivity improvement through autonomous operations, and one mine saw a 30% increase.

Leading robotics vendors, meanwhile, are building autonomous orchestration frameworks for coordinating the actions of individual robots with each other and the humans they work with. The orchestration enables a large swarm of robots to go from moving like individual birds to a coordinated flock flying in unison. The multiple systems work together to create a new outcome that would not be possible by any lone system.

"As the use of robotics becomes more prolific, it will become more important to streamline the integration between different robotics," said Akash Gupta, CTO of GreyOrange, a warehouse robotics vendor.

"Integration is the first step towards orchestration, which is to get multiple systems working together to create a new outcome that would not be possible by any lone system," he explained, predicting a rise in demand for platforms that reduce the time required to integrate robotics systems individually.

The push for easier integration and orchestration of autonomous systems is also happening in the skies. Increasingly capable drones are monitoring construction progress, keeping tabs on farms and delivering packages. Early operations require humans to track these flying robots at every step of their flight. But new FAA rules for Unmanned Aircraft Systems Beyond Visual Line of Sight could help create frameworks for autonomous orchestration at scale. In addition to paving the way for broader adoption of drones, these early frameworks could down the road help enterprises figure out how coordinate their other autonomous enterprise systems.

Issues related to orchestration and scaling also loom large in efforts to build autonomous business processes. Business process management (BPM) is an age-old discipline that takes a structured approach to improving the processes organizations use to get work done, serve their customers and generate business value.

In recent years, RPA has captured the attention of BPM experts because of its ability to mimic the way humans interact with software apps automatically. The early RPA software robots, called bots, were initially only capable of automating the simplest tasks. Even small changes in the process caused these relatively brittle bots to break. RPA vendors such as UiPath, Automation Anywhere and Blue Prism have worked on improving bot resilience. They've also applied AI techniques such as optical character recognition, natural language processing and machine vision to increase the ability of RPA bots to make decisions.

But scaling these bots to take on the automation of complex business processes proved a hard nut to crack. Indeed, IT consultancy Forrester Research found in 2019 that most firms had fewer than 10 bots in production. While each bot was becoming more capable and autonomous, it was clear that a better approach was required to scale not only bots but also all types of enterprise automation in general.

Research firm Gartner coined the term hyperautomation to characterize an approach that combines new automation capabilities with tools for scaling and orchestrating automation across the enterprise. In practice, hyperautomation is not limited to scaling RPA but can be applied to low-code/no-code platforms and enterprise applications for ERP, CRM and supply chain management.

"Companies often execute multiple hyperautomation initiatives as a part of their overall transformation, modernization and innovation strategy," said Ed Macosky, senior vice president and head of product for software company Boomi. "This usually involves AI/ML technologies in various form factors for process discovery, process mining, intelligent document processing, decision management and more."

Hyperautomation's panoply of technologies are applied across three types of autonomous functionality -- guidance, scaling and orchestration:

New approaches and improvements to autonomous guidance, scalability and orchestration of business processes will eventually transform other aspects of IT, such as operation, security and testing.

IT service management teams have been early leaders in any form of automation. Various IT service management (ITSM) and incident response systems have evolved to help small teams manage ever-larger infrastructure and business requirements quickly and efficiently, most recently with the addition of AI techniques.

In 2016, Gartner coined the term AIOps to characterize the addition of machine learning and big data to assist IT operations. The main idea was that better analytics could help triage false alarms more efficiently, identify problems sooner and guide teams to the root cause of complex issues.

Since then, others have started to explore how better intelligence could not only identify problems but also automatically fix them. Oracle launched its Autonomous Data Warehouse and Autonomous Transaction Processing in 2018. In this implementation, the autonomous capabilities help tune the database and optimize indexes on the fly. In addition, the software automatically applies patches in response to the discovery of new security vulnerabilities. Oracle later extended autonomous capabilities to its PaaS, integration and application development workloads.

More recently, ITSM vendors such as BigPanda and application performance monitoring vendors like Dynatrace have begun exploring how AIOps can be applied to autonomous operations more broadly. A recent survey conducted by Dell and Intel on autonomous operations adoption found that 90% of ITSM teams were struggling as a result of the time spent on repetitive manual tasks involving steps that could be automated and function autonomously.

Autonomous systems need to be able to adjust to new feedback automatically yet safely roll back if problems are detected. For instance, Uber uses machine learning algorithms to predict traffic on New Year's Eve, and e-commerce companies like Amazon do the same to predict Black Friday shopping trends. This allows these companies to set up automated systems that scale servers up or down depending on the traffic loads, thus preventing or mitigating expensive outages and downtimes.

"The ability to handle these and automatically detect and roll back changes has become crucial for an enterprise," Ganesh, of Anyscale, said.

Today, if a change made in the configuration causes a website to load more slowly in a specific geographic region, an automated system detects this and rolls back to the last known good state. Previously, this would involve waking up a human who then needed to understand where the system is failing, figure out the offending change and then roll it back. This often took hours and led to frustrated users and loss in revenue.

Syntax, a cloud consultancy, recently worked on a related use case: The autonomous optimization of disk I/O throughput for mission-critical applications. The new system analyzes customer disk I/O usage and automatically scales throughput up and down throughout the month, allowing customers to have the best possible disk performance while minimizing their total cost.

"Imagine you can free up your data center and hardware specialists by not only moving to the cloud but also by adding an autonomous management layer that optimizes your computing resources automatically at the best cost-benefit factor," said Jens Beck, director of IIoT, analytics and innovative cloud services at Syntax.

The move toward an autonomous data center could enable an autonomous service desk that removes high-volume, simple and repetitive tasks from the day-to-day duties -- such as ticket routing, password resets and user extensions. This frees up entry-level service desk employees to take on service desk tasks, which helps teams scale without necessarily adding headcount, Beck said.

Marcelo Tamassia, global CTO at Syntax, said the use of autonomous capabilities to increase service agility and resource efficiency has the potential to significantly improve IT service delivery.

"Historically, traditional automation has been created mainly to optimize existing processes. However, the proliferation and availability of APIs paired with the rise of AI and ML services have created a unique opportunity to develop autonomous solutions to traditional use cases," Tamassia said.

Security teams also need to consider how autonomous systems can help them address increasingly sophisticated -- and autonomous -- threats. For example, the onslaught of DDoS attacks demonstrates the sheer power of autonomous botnets built on simple devices like video recorders and cameras.

Meanwhile, other hackers are using sophisticated bots designed to mimic humans for bad ends, including scalping tickets, buying up Sony PlayStation consoles and spreading misinformation across social media.

Down the road, these threats could possess the intelligence to outwit even the most sophisticated defenses as the raw power of a mob is fortified by improvements in guidance, scale and orchestration. Autonomous security systems will need to evolve to keep up with this possibility.

The shift to cloud computing only increases the need for autonomous security systems. As organizations continue to move operations to the cloud, the threat surface gets increasingly complex with the addition of access points for trading partners, employees, supply chains and third-party applications.

It is like building a house with a thousand locks, ripe for the picking, said Mike O'Malley, senior vice president of strategy at SenecaGlobal, an IT outsourcing and advisory firm. Autonomous security could help protect these systems at scale, he said.

There are several ways that autonomous security capabilities can improve workflows in terms of enhanced event detection and response, faster software supply chain updates and more nuanced API security capabilities. These include the following:

The challenge for CIOs will lie in finding opportunities to extend autonomous capabilities across different types of tools and systems. Each tool used for improving the value of robots, RPA, security and IT will come with its own best practices and data formats.

Similarly, teams will need to identify how improvements across autonomous guidance, scalability and orchestration could be synergistically combined. For example, process mining tools currently used to scale RPA could be adapted to scaling security operations and IT operations. Traditional integration for exchanging data and aligning data schemas is challenging enough. Autonomous systems will also need to align control across disparate systems.

In addition, teams will need to scale their risk management capabilities to identify and plan for unforeseen risks generated by the autonomous systems. A strategy for rooting out bias in AI and increasing AI explainability could help here.

But at the end of the day, it is up to execs to make decisions that benefit the business, employees and society. This extends to the decisions made by autonomous systems. "It's important to determine if the decision the system is making is causing unintended consequences," O'Malley said.

Michel Chabroux, senior director of product management at software provider Wind River, said enterprises need to consider many factors when adopting autonomous systems, including reliability, liability, regulatory issues and societal concerns about potential job losses.

Teams also need to consider the unintended consequences of hyperscale automation run amok.

The paperclip problem introduced by philosopher Nick Bostrom in 2014 illustrates the inherent danger. Bostrom theorized that a superintelligent AI programmed to make paperclips more efficiently could squander other resources and start wars to achieve its goal. A more likely scenario today is that the fulfillment of a short-sighted business goal might accidentally run up a large cloud bill, create an inhospitable environment for employees or impact sustainability goals.

In the long run, advances in autonomous systems will need to be balanced with tools -- such as digital twins and the metaverse -- that provide a window into how automation is playing out. Improved context will help humans more quickly detect emerging problems and provide better guardrails for autonomous systems that make decisions at scale.

Read the rest here:

The push is on to build the autonomous enterprise - TechTarget

The Week Ending June 10

Topping this weeks Came to Win list is Cisco Systems for providing a comprehensive blueprint for its security cloud offerings.

Also making this weeks list are Pure Storage and Nvidia for launching the next generation of their AI-ready infrastructure and Palo Alto Networks for providing a look at its new autonomous Security Operations Center technology.

Data security startup Immuta is on this weeks list for a successful funding round. And cloud giant Google makes the list for the ongoing expansion of its network of data centers to meet customers growing cloud application needs.

Cisco made several significant moves in the security realm this week, detailing its strategy to help enterprises connect their entire security architecture via a new platform and debuting the highly anticipated Cisco Plus XaaS SASE (Secure Access Service Edge) offering.

Cisco unveiled those and other security capabilities and services at this weeks RSA 2022 conference.

The Cisco Security Cloud, a unified, open standards-based platform, is the basis for the companys future security architecture. The platform will include threat prevention, detection, response and remediation at scale and will integrate with third-party technologies. Its intended to ensure security across hybrid and multi-cloud environments with capabilities for securely connecting people, applications and devices located anywhere.

The new Cisco Plus Secure Connect Now, a unified security and network offering, provides partners and customers with a turnkey SASE system. Built on the Meraki platform, Cisco Plus Secure Connect Now is the latest offering in the companys Cisco Plus as-a-service strategy.

Flash storage developer Pure Storage and technology partner Nvidia this week unveiled the next generation of their AIRI AI-ready infrastructure that combines Pure Storages new FlashBlade//S: array with Nvidias DGX A100 GPU systems.

The companies said the new AIRI//S provides the compute and data storage muscle needed for heavy-duty AI workloads.

Pure Storage and Nvidia touted the AIRI//S as a simple, on-demand system aimed at accelerating AI initiatives, particularly projects that traditionally required discrete server clusters for data analytics for AI development, model training and inferencetasks that a single system running the DGX A100 can do.

Pure also unveiled the new FlashBlade//S: at its Pure//Accelerate Digital techfest22 in Los Angeles this week. The modular version of the companys FlashBlade file and object storage array for the first time disaggregates compute and capacity to allow customers to scale each as needed.

In addition, Pure made a move to expand its Evergreen storage subscription service by offering customers the ability to buy separate subscriptions for the companys hardware and software.

Data security and access technology developer Immuta raised an impressive $100 million in a Series E funding round this week, boosting its total funding to $267 million.

The round was led by venture capital firm NightDragon. But also notable was the fact that Snowflake Ventures, the venture capital arm of data cloud company Snowflake, was a new investor in Immuta.

The funding comes as Immuta prepares to accelerate deployment of its technology across all the prominent cloud platforms. The company intends to apply the new funding to possible acquisitions, research and development, expansion of sales and marketing teams, and deepening strategic partnerships within the cloud-data ecosystem.

Cisco wasnt the only company making big announcements at the RSA Conference this week. At the event Palo Alto Networks said it has developed autonomous SOC technology that will make Security Operations Centers more automated and less reliant on humans.

Speaking at the conference, Palo Alto Networks founder and CTO Nir Zuk (pictured) said the company is working with about 10 early adopter design partners that have installed the new technology within their SOCs.

Zuk said the system relies on AI and machine learning technologyand less on human oversightto detect and prevent cyberattacks. Palo Alto Networks is using the new system in its own SOC where it has dramatically reduced the number of duplicative alerts about possible security breaches.

Google Cloud launched a new data center in Dallas this week, marking the companys 11th region in North America and its 34th availability zone across the globe.

The new Dallas facility is part of Google Clouds $9.5 billion commitment to boost its data center footprint, a key component of Google Clouds cloud computing growth strategy that also includes a cybersecurity road map, revamping the Google Cloud Marketplace and eradicating channel partner conflict.

The new Dallas data center will boost Googles ability to support high-performance applications and workloads from partners and customers and improve service availability and business continuity.

Customers can integrate their on-premises workloads with the data center using Cloud Interconnect or multi-cloud options using Anthos. They also gain access to such systems as Google Kubernetes Engine, Cloud Storage, Persistent Disk, Cloud SQL and Cloud Identify.

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5 Companies That Came To Win This Week June 10 - CRN

The technology sector saw the worst start to a year since 2002 as the giants were hit hard by soaring yields and the Federal Reserves tightening monetary. In fact, big technology stocks are facing their biggest rout in more than a decade. The S&P 500s information-technology sector has dropped 20% so far this year.

This has prompted investors to shell a record $7.6 billion this year from technology-focused mutual funds and ETFs through April, according to Morningstar Direct data going back to 1993. The ultra-popular Select Sector SPDR Technology ETF XLK pulled out more than $179 million in capital (read: 6 Reasons Why Tech ETFs May Rebound Soon).

As a result, inverse or inverse-leveraged ETFs have been on the rise as these fetch outsized returns on bearish sentiments in a short span. Daily Dow Jones Internet Bear 3X Shares WEBS, MicroSectors FANG & Innovation -3x Inverse Leveraged ETN BERZ, Direxion Daily Cloud Computing Bear 2X Shares CLDS, Direxion Daily Technology Bear 3x Shares TECS and ProShares UltraShort Technology REW have been outperforming and might continue their strong performance if sentiments remain the same.

The central bank took the most aggressive policy action in decades to combat soaring inflation by raising rates by 50 bps last month and pushing the benchmark above 0.75%. The hike marked the biggest interest-rate increase since 2000, resulting in soaring yields. The 10-year Treasury yields have jumped to the highest level since 2018 to above 3%. The tech sector relies on easy borrowing for superior growth and its value depends heavily on future earnings. A rise in long-term yields lowers the present value of companies future earnings, sparking fears of overvaluation (read: Why High Dividend ETFs are Beating the Market).

Further, many of the trends that flourished over the past two years including bullish options trades, special-purpose acquisition companies and cryptocurrencies have made a sharp U-turn, thereby dampening the appeal of the technology stocks. Many investors think that the combination of factors has put an end to the the decade-long era of tech dominance in markets.

Story continues

Leveraged and inverse-leveraged ETFs either create a leveraged long/short position, an inverse long/short position or a leveraged inverse long/short position in the underlying index through the use of swaps, options, futures contracts and other financial instruments. Due to their compounding effect, investors can enjoy higher returns in a short period provided the trend remains a friend.

However, these funds run the risk of huge losses compared to traditional funds in fluctuating or seesawing markets. Further, their performance could vary significantly from the actual performance of their underlying index over a longer period when compared to a shorter period (such as weeks or months).

Investors should note that these products are suitable only for short-term traders as these are rebalanced on a daily basis. Further, liquidity can be a big problem as it can make the products more expensive than they appear (see: all the Inverse Equity ETFs here).

Still, ETF investors seeking to tap abrupt movements can go long or short in the near term.

Daily Dow Jones Internet Bear 3X Shares (WEBS) Up 127.7%

Daily Dow Jones Internet Bear 3X Shares provides three times inverse play on the Internet corner of the broad technology sector by tracking the Dow Jones Internet Composite Index.

Daily Dow Jones Internet Bear 3X Shares has attracted $40.7 million in its asset base and charges 95 bps in annual fees. The ETF sees an average daily volume of about 335,000 shares.

MicroSectors FANG & Innovation -3x Inverse Leveraged ETN (BERZ) Up 68.2%

MicroSectors FANG & Innovation -3x Inverse Leveraged ETN is linked to the three times leveraged inverse performance of the Solactive FANG Innovation Index. The index tracks the stock prices of 15 large-capitalization, highly liquid U.S. technology stocks.

With AUM of $9 million, MicroSectors FANG & Innovation -3x Inverse Leveraged ETN has an expense ratio of 0.95% and trades in an average daily volume of 80,000 shares.

Direxion Daily Cloud Computing Bear 2X Shares (CLDS) - Up 44.2%

Direxion Daily Cloud Computing Bear 2X Shares targets the cloud-computing segment of the broad technology sector, offering two times inverse exposure to the performance of the Indxx USA Cloud Computing Index.

With AUM of $19.5 million, Direxion Daily Cloud Computing Bear 2X Shares has an expense ratio of 0.95% and trades in an average daily volume of 12,000 shares.

Direxion Daily Technology Bear 3x Shares (TECS) - Up 42%

Direxion Daily Technology Bear 3x Shares provides three times inverse exposure to the daily performance of the Technology Select Sector Index.

Direxion Daily Technology Bear 3x Shares has amassed about $123.6 million in its asset base while charging 95 bps in fees per year from investors. Volume is solid as it exchanges around 3 million shares a day on average (read: Inverse ETFs Take Flight as Market Turns Sour).

ProShares UltraShort Technology (REW) Up 41.7%

ProShares UltraShort Technology offers two times the inverse of the daily performance of the Dow Jones U.S. Technology Index, which measures the performance of the companies, including those involved in computers and office equipment, software, communications technology, semiconductors, diversified technology services and Internet services.

ProShares UltraShort Technology has accumulated $12.4 million in its asset base and charges 95 bps in annual fees. It trades in an average daily volume of nearly 64,000 shares.

While the strategy is highly beneficial for short-term traders, it could lead to huge losses compared with the traditional funds in fluctuating markets. Due to their compounding effect, investors can enjoy higher returns in a short period of time, provided the trend remains a friend.

Further, their performance could vary significantly from the actual performance of the underlying index over the longer period compared to a shorter period (such as weeks or months).

Want the latest recommendations from Zacks Investment Research? Today, you can download 7 Best Stocks for the Next 30 Days. Click to get this free reportTechnology Select Sector SPDR ETF (XLK): ETF Research ReportsProShares UltraShort Technology (REW): ETF Research ReportsDirexion Daily Technology Bear 3X Shares (TECS): ETF Research ReportsDirexion Daily Dow Jones Internet Bear 3X Shares (WEBS): ETF Research ReportsDirexion Daily Cloud Computing Bear 2X Shares (CLDS): ETF Research ReportsMicroSectors FANG & Innovation 3X Inverse Leveraged ETN (BERZ): ETF Research ReportsTo read this article on Zacks.com click here.Zacks Investment Research

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5 Inverse ETFs Rallying This Year With Tech Meltdown - Yahoo Finance