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Exam Code: A2180-376 Practice test 2022 by team
Accessment: IBM WebSphere MQ V7.0, Solution Design
IBM Accessment: test Questions
Killexams : IBM Accessment: test Questions - BingNews Search results Killexams : IBM Accessment: test Questions - BingNews Killexams : As NIST Prepares For Quantum Safe Security, IBM Rolls Out Support

The world of cryptography moves at a very slow, but steady pace. New cryptography standards must be vetted over an extended period and therefore new threats to existing standards need to be judged by decades-long timelines because updating crypto standards is a multiyear journey. Quantum computing is an important threat looming on the horizon. Quantum computers can solve many equations simultaneously, and based on Shor’s Algorithm, crypto experts estimate that they will be able to crack asymmetric encryption. In addition, Grover’s algorithm provides a quadratic reduction in decryption time of symmetric encryption. And the question these same crypto experts try to answer is not if this will happen, but when.

Today’s crypto algorithms use mathematical problems such as factorization of large numbers to protect data. With fault-tolerant quantum computers, factorization can be solved in theory in just a few hours using Shor’s algorithm. This same capability also compromises cryptographic methods based on the difficulty of solving the discrete logarithm problems.

The term used to describe these new, sturdier crypto standards is “quantum safe.” The challenge is we don’t know exactly when fault-tolerant quantum computers will have the power to consistently break existing encryption standards, which are now in wide use. There’s also a concern that some parties could get and store encrypted data for decryption later, when suitably capable quantum computers are available. Even if the data is over ten years old, there still could be relevant confidential information in the stored data. Think state secrets, financial and securities records and transactions, health records, or even private or classified communications between public and/or government figures.

U.S. Department of Commerce’s National Institute of Standards and Technology (NIST) believes it’s possible that RSA2048 encryption can be cracked by 2035. Other U.S. government agencies and other security-minded entities have similar timelines. Rather than wait for the last minute to upgrade security, NIST started a competition to develop quantum-safe encryption back in 2016. After several rounds of reviews, on July 5th of this year, NIST chose four algorithms for the final stages of review before setting the standard. IBM developed three of them, two of those are supported in IBM’s Z16 mainframe today.

NISTNIST Announces First Four Quantum-Resistant Cryptographic Algorithms

The new IBM crypto algorithms are based on a family of math problems called structured lattices. Lattice problems have a unique characteristic that will make it reasonably difficult to solve with quantum computing. Structured lattice problems require solving for two unknowns – a multiplier array and an offset and is extremely difficult for quantum computing to solve the lattice problems. The shortest vector problem (SVP) and the closest vector problem (CVP) – upon which lattice cryptography is built – is considered extremely difficult to a quantum computer to solve. Each candidate crypto algorithm is evaluated not just for data security, but also for performance - the overhead cannot be too large for wide spread use.

The final selections are expected in 2024, but there’s still a chance there will be changes before the final standards are released.

MORE FROM FORBESIBM Lattice Cryptography Is Needed Now To Defend Against Quantum Computing Future

IBM Supports Quantum Safe in New Z-Series Mainframes

IBM made a strategic bet before the final NIST selections. The recently released IBM Z16 Series computers already support two of the final four quantum safe crypto candidates: the CRYSTALS-Kyber public-key encryption and the CRYSTALS-Dilithium digital signature algorithms. IBM is set to work with the industry to substantiate these algorithms in production systems. Initially, IBM is using its tape drive storage systems as a test platform. Because tape is often used for cold storage, it's an excellent medium for long-term data protection. IBM is working with its client base to find the appropriate way to roll out quantum-safe encryption to the market. This must be approached as a life cycle transformation. And, in fact, IBM is working with its customers to create a crypto-agile solution, which allows the exact crypto algorithm to change at any point in time without disrupting the entire system. It’s not just a rip and replace process. With crypto-agility, the algorithm is abstracted from the system software stack so a new algorithms can be deployed seamlessly. IBM is developing tools making crypto status part of the overall observability with a suitable dashboard to see crypto events, etc.

These new algorithms must be deployable to existing computing platforms, even at the edge. However, it's not going to feasible to upgrade every system; it’s probably going to be an industry-by-industry effort and industry consortia will be required. For example, IBM, GSMA (Global System for Mobile Communication Association), and Vodafone recently announced they will work via a GSMA Task Force to identify a process to implement quantum-safe technologies across critical telecommunications infrastructure, including the networks underpinning internet access and public utility management. The telecommunication network carries financial data, health information, public-sector infrastructure systems, and sensitive business data which needs to be protected as it traverses global networks.

IBM Research BlogHow IBM is helping make the world's networks quantum safe | IBM Research Blog

What’s Next for Quantum Safe Algorithms

Fault-tolerant quantum computing is coming. When it will be available is still a guessing game, but the people who most care about data security are targeting 2035 to have quantum-safe cryptographic algorithms in place to meet the threat. But that’s not good enough. We need to start protecting critical data and infrastructure sooner than that, considering the length of time systems are deployed in the field and data is stored. Systems such as satellites and power stations are not easy to update in the field.

And there’s data that must be stored securely for future retrieval, including HIPAA (for medical applications), tax records, toxic substance control act and clinical trial data, and others.

Even after the deployment of these new algorithms, this is not the end – there may still be developments that can break even the next generation quantum-safe algorithms. The struggle between those that want to keep systems and data safe and those that want to crack them continues and why companies should look to building in crypto agility into their security plans.

Tirias Research tracks and consults for companies throughout the electronics ecosystem from semiconductors to systems and sensors to the cloud. Members of the Tirias Research team have consulted for IBM and other companies throughout the Security, AI and Quantum ecosystems.

Fri, 07 Oct 2022 11:36:00 -0500 Kevin Krewell en text/html
Killexams : Risk Prioritisation with Attack Surface Management (Guest blog by IBM)

Guest blog by Scott Haddow, Security Client Exec at IBM #Cyber2022

Cyber Security loves buzzwords, but they get over-exposed faster than Kevin Hart.  Looking at you, Zero-Trust.

If you haven't heard about Attack Surface Management (ASM) yet, you will.  But bear with me, because that's not a bad thing. 

ASM is still on the 'shiny and new' slope of Gartner's hype cycle, but it's already real and out in the wild keeping organisations safer. 

From cloud migrations to IoT integration and hybrid working, IT environments are changing fast – and that’s also true for their attack surfaces, leading to poor visibility of risk in real-time.

Q/ When is the best time to find weaknesses?

A/ Before an attack happens. 

It’s a beautiful concept that may seem naïve when measured against the daily reality of a SOC.  Cyber-nirvana is the goal but hard to get there if you’re stamping out fires all day.  

And this is where we approach the paradox at the heart of cyber security today, having largely abandoned the concept of the completely defensible perimeter.  Almost all the technologies in the SOC are designed to spot things after they happen; that is – after the threat actor is doing something we don’t want them to do inside our network.  AI and machine learning driven uber-suites of clever code that spot, correlate and jump on those trails before they snowball into a full-blown cyber heist.   I’m not suggesting that we don’t need all of that and a perimeter – because we do, but threat actors understand our defenses and are finding ways to slip under the radar of reactive security tooling in a never-ending game of cat and mouse. 

There’s a lot of different numbers for the average dwell time of an attacker before an event like ransomware detonation – but broadly the numbers agree that it’s more than 100 and less than 300 days.  That’s a long time to deliver someone to figure out how your operation works.

Add to that that only about 1 in 5 enterprises can monitor their attack surfaces for changes in real time, or to put it another way, four fifths of the world’s enterprises can’t.    

The old question of how you would break into your own home if you were locked out is useful here but falls short when describing the cyber-attack surface, because you’d need to talk about windows you didn’t know you had and prevent an attacker from getting in through a plughole. 

The typical attacker has a laptop, some tools and an internet connection when they begin looking for a way in.  But it’s best not to underestimate our adversary – marketing shows us a lot of people in hoodies hunched over laptops, but it would be scarier to show a 24x7 Ransomware-aaS operation in the C2C marketplace.   This is organized and profitable crime, but regardless of the maturity of the organisation or individual attacking you, they begin on the outside of your environment.  What they look for is internet facing services, IPs, domains, networks, hostnames and so on.    In the process they will uncover your shadow IT, forgotten assets (like that test/dev environment everyone assumed someone else tore down), and other blind-spots and process failures, for example a brute-forceable and exposed login applet, or down-rev web server.  Those are the chinks in the armour offering a route in, and because they face out into the internet, they are highly tempting.  But (of course) they can’t be fixed until you know about them – which means that we need to wait for the lights to blink on the big reactive dashboard, and then you’ve got another fire to stamp out.

If we want to move to a proactive posture, using an Attack Surface Management tool like IBM Randori which scopes your attack surface like an attacker is a smart move.  If we can see what they see when they look at us from the outside, then we have a prioritized inventory of attack risk. 

That’s important because the last thing anyone needs is a report with an overwhelming list of to-do items on it, because we’re already putting out fires as it is.  Having issues ranked by their ‘temptation score’ lets the SOC team focus on the urgent fixes, and then schedule work on the less urgent stuff.  And because Randori only looks at your attack surface from the outside, it’s agentless and doesn’t need appliances.

Having a prioritized inventory of risk let’s you find those open windows and close them, which makes it harder for the attacker to get in. 

Nobody wants to be over exposed, not even Kevin Hart.

IBM has acquired Randori, a leading Attack Surface Management provider and recently named a cool vendor by Gartner.  Although ASM is an emerging technology IBM has never been afraid to be at the forefront of innovation. Find out more here

Help to shape and govern the work of techUK’s Cyber Security Programme

Did you know that nominations are now open* for techUK’s Cyber Management Committee? We’re looking for senior representatives from cyber security companies across the UK to help lead the work of our Cyber Security Programme over the next two years. Find out more and how to nominate yourself/a colleagues here.

*Deadline to submit nomination forms is 17:00 on Tuesday 18 October.

Upcoming events 

Cyber Innovation Den

On Thursday 3 November, techUK will host our fourth annual Cyber Innovation Den online. This year we’ll explore efforts being made to realised the ambition set out in the National Cyber Strategy, with speakers taking a look at the progress we’ve seen to date, including the foundation of the UK Cyber Security Council, the reinvigoration of the Cyber Growth Partnership and the continued growth in the value of the sector to the UK economy.

Book now!

Cyber Security Dinner

In November techUK will host the first ever Cyber Security Dinner. The dinner will be a fantastic networking opportunity, bringing together senior stakeholders from across industry and government for informal discussions around some of the key cyber security issues for 2022 and beyond.

Book now!

Get involved

All techUK's work is led by our members - keep in touch or get involved by joining one of the groups below.

The Cyber Security Group provides a coherent voice for industry working in "high threat" areas - including defence, national security and resilience, the protection of critical national infrastructure, intelligence and organised crime.

The Cyber Management Committee sets the strategic vision for the cyber security programme, helping the programme engage with government and senior industry stakeholders.

The CSSMEF is comprised of SME companies from the techUK membership. The CSSMEF seeks to include a broad grouping of different SME companies working in the Cyber Security (CS) sectors.


Scott Haddow

Security Client Exec, IBM

Thu, 13 Oct 2022 20:13:00 -0500 text/html
Killexams : Meet the Woman Behind ‘AI for Her’

Ideas are crucial for spurring innovations in products and services. One such innovative leader is Heena Purohit, who plays a major role in providing teams with a framework for maturing their ideas into products for customers. 

Analytics India Magazine interacted with Purohit, head of product at IBM. She is the product lead at IBM’s internal incubator program, which enables IBMers to bring innovative ideas and solutions to the real world. Purohit founded ‘AI for Her’ — a 501(c)(3) non-profit organisation on a mission to bring more women and gender minorities into AI. By reducing the AI diversity gap, it helps to build AI systems that are fair and unbiased.

“A typical day for me involves working with various venture teams that deal with emerging technologies and advising them on their product strategy and execution. This includes tasks such as designing and helping teams execute experiments to ensure the venture would be a viable business for IBM, coaching teams on product thinking or, if needed, rolling up my sleeves and supporting the teams in building customer collateral, lead customer/user interviews, and support sales execution.”

Transforming ideas into products 

Purohit says that no two days are alike at work, especially since each venture team is different and is solving a unique customer problem in a distinct way. “And that’s the most fun part of my job!”

She says, “My program is open for IBMers around the world, providing an opportunity to surface and test the best ideas across IBM. This helps in facilitating a culture of innovation and intrapreneurship within the company, while helping IBM build and launch the next generation of products, faster.” She adds that for many teams submissions are ideas and technologies they’ve been working on for years. Being selected in the program finally gives them the opportunity to put their experiments to work and see if they’re actually viable.

Implementing AI in product management

In her initial years at IBM, Purohit built AI solutions for industrial customers driving the strategy, product management and design to help manufacturing clients. She holds an undergraduate degree in telecommunications engineering from the University of Mumbai, where she built solutions involving IoT data/sensors. She also holds a double-major MBA from the University of Notre Dame. “So during my MBA, I came across a brand-new business that IBM was launching (Watson IoT), I applied and was selected for a product management internship role in the unit.”

Purohit says, like many others, she fell into AI following her interests. “While IoT sensors were a disruptive way to get more data than ever, I felt gravitated towards the part where you analyse the data – from IoT sensors, or other structured and unstructured data – to unlock new insights. I was fascinated by how AI/ML technologies enabled us to do that in new ways.” 

She said her AI journey began during this period, where she had the opportunity to launch and scale vertical AI solutions for industrial customers. The experience gave insights into having a firsthand look at the opportunities and challenges that customers are facing in adopting AI across the company. Having worked with some of the most brilliant AI minds at the company, Purohit says that her passion for emerging AI technologies and solving key business problems only grew.

‘You don’t have to be a data scientist to understand or benefit from AI’

Having worked with several customers and mentees to help them adopt AI or get into AI roles, Purohit shares one of her biggest learnings, “You don’t have to be a data scientist to understand or benefit from AI. This is also freeing because as AI technologies get more and more accessible, it helps bring more diverse voices in AI discussions, getting them to help build an AI-powered future.”

More specifically to her experience working on emerging technologies, Heena says that this is a great time to build a product. “To get started, think about all the tasks you do in your personal and professional life. Identify the manual/repetitive/mundane tasks and think if AI can help you Excellerate that experience. If the answer is yes, try it out. It’s very likely that there’s a no-code AI tool out there for you to prototype this. This way, you’re not only dipping your toes in AI but also gaining the experience in using AI tools, so that you can then move on to solving bigger and better problems with AI.”

Purohit has been on a mission to make AI more accessible and help bring more people into the AI industry. She adds that this has manifested into the decisions made around which products to lead. Outside her current role at IBM, Purohit has spoken at over 20 events on the topic, and been published in over 15 books and articles. The tech leader has actively judged AI solutions at 6 hackathons. “The ‘AI for Her’ content on getting into AI and AI literacy has impacted over 12,000 students. And this feels like just the beginning.”

Find your tribe of cheerleaders and supporters

Purohit shares that across all channels, the biggest takeaways have come from the questions since they provide insight into the pain points that people are facing today. “These challenges often don’t pertain to the technical skills gap but around the mindset shift. And that’s why in most of the sessions I’ve delivered, we end up touching upon syllabus such as imposter syndrome, knowing your worth, and finding your tribe of cheerleaders and supporters,” she said.

She credits IBM for providing an incredible network of mentors that inspired and gave her opportunities to grow. “Perfect segway from the mindset shift because I faced that as I moved up in my career trajectory, too. It’s important to acknowledge that one doesn’t get to where they are without the help of others. I’m grateful for the support I’ve received, and it’s also why I feel passionately about paying it forward.”

Purohit was recognised as one of the Top 11 ‘Women AI Leaders’ at RISE 2020 and Datatech Vibe’s 2021 ‘Top Women Leaders In AI To Watch’. She was awarded the University’s Alumni Award in 2019 for her impact on women in technology initiatives. “I feel incredibly thankful for both recognitions and am honoured to be mentioned alongside many of the women I admire. I want to talk about how this happened. At the start of the pandemic, when everyone was in strict lockdown, I missed my break room conversations where I’d catch up with my colleagues and geek out on AI. I raised this on one of my favourite Women in Tech Facebook groups and turns out, many other women missed this, too.”

Purohit says that this led to the establishment of ‘AI for Her’. “This gave me the confidence to take this further and we’re now a 501c3 nonprofit on a mission to reduce the gender gap in the AI industry today and amplify the message that everyone can get into AI. We’ve been brewing some even more exciting things this year and looking forward to the launch!”

Take Five

1.    Favourite thing in the ML/AI industry today & why? I’m incredibly excited about foundation models. Having tested various foundation models, I can attest that not only are they better than anything else I’ve used, but also they’re equally flawed. So, while I know we have a way to go before foundation models become usable, I’m excited by how they could transform many areas of our lives today

2.    Top three apps you frequently use: YouTube, Elevate, and Reddit

3.    Favourite book on AI: Weapons of Math Destruction 

4.    Favourite podcast in AI and ML: I prefer non-AI podcasts to bring in more diversity to my day-to-day life. I love Exponent (Ben Thomson’s podcast on tech business analysis) and Acquired (Great storytelling and startup analysis)

5.    What would you have been doing if you weren’t a Product Head? I would certainly still be in tech. But as a consultant or a Product Owner

6.    How do you define your leadership style? I strive to lead with empathy. Since the pandemic, this has become more important to me than ever  

Thu, 13 Oct 2022 23:40:00 -0500 en-US text/html
Killexams : International Business Machines Corp

52 week range

114.56 - 144.73

  • Open121.80
  • Day High122.88
  • Day Low121.43
  • Prev Close120.04
  • 52 Week High144.73
  • 52 Week High Date06/06/22
  • 52 Week Low114.56
  • 52 Week Low Date11/26/21
  • Market Cap109.754B
  • Shares Out903.18M
  • 10 Day Average Volume4.46M
  • Dividend6.60
  • Dividend Yield5.43%
  • Beta0.83
  • YTD % Change-9.08


  • Open121.80
  • Day High122.88
  • Day Low121.43
  • Prev Close120.04
  • 52 Week High144.73
  • 52 Week High Date06/06/22
  • 52 Week Low114.56
  • 52 Week Low Date11/26/21
  • Market Cap109.754B
  • Shares Out903.18M
  • 10 Day Average Volume4.46M
  • Dividend6.60
  • Dividend Yield5.43%
  • Beta0.83
  • YTD % Change-9.08


  • EPS (TTM)6.42
  • P/E (TTM)18.92
  • Fwd P/E (NTM)12.46
  • EBITDA (TTM)11.935B
  • ROE (TTM)27.73%
  • Revenue (TTM)59.677B
  • Gross Margin (TTM)54.01%
  • Net Margin (TTM)9.61%
  • Debt To Equity (MRQ)259.21%


  • Earnings Date10/19/2022
  • Ex Div Date08/09/2022
  • Div Amount1.65
  • Split Date-
  • Split Factor-

Thu, 13 Oct 2022 11:59:00 -0500 en text/html
Killexams : Lewis and Clark County clerk candidates offer contrasting views

The Lewis and Clark County clerk, recorder and treasurer’s race pits an incumbent who has years of experience within the department and cites accomplishments from her short time heading the office against a challenger who touts her business resume and says she puts integrity at the top of her list.

Both say they are dedicated to serving others.

The office carries a big load when it comes to county government as it includes accounting, elections, motor vehicles, property tax and records. There are 31.5 full-time employees and about 13 short-term employees for the various elections, and the department has about 300 election judges for the primary election. The budget for all five departments is just over $3.5 million.  

The race is nonpartisan. Election Day is Nov. 8. Ballots were sent out Oct. 14.

Incumbent Amy Reeves, an employee with 27 years in the clerk’s office and who has worked in every division within the department, was appointed to the county clerk position in 2020 by the Lewis and Clark County commissioners when Paulette DeHart retired.

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“I like my job,” she says.

Challenger Bettijo Starr points to her experience in the world of business and finance including jobs with the Internal Revenue Service, with IBM and in real estate banking, and she makes election integrity a clarion call of her campaign.

“Integrity is doing the right thing, even when nobody is looking,” she said during an Oct. 6 candidates’ forum at Hometown Helena.

Reeves, 50, said her experience has prepared her for running the department. She notes she understands mill levies, has plenty of property tax experience and has worked with 40 school districts and special districts in elections.

“I’ve worked on elections my whole career,” she said.

She also faced many challenges when she took the reins of the department. In response to an Independent Record survey, she noted that there had long been public frustration in the Motor Vehicle Department due to wait times to receive service.

She said the population had outgrown the number of qualified staff. This remodel of the first floor of the City/County Building resulted in four more motor vehicle windows to reduce wait times and the backlog of title work. It now includes four windows that comply with the Americans with Disabilities Act.

She said staffing shortages have plagued all five divisions in the Treasurer/Clerk & Recorders Office, adding there has been staff turnover ranging from one-third to two-thirds in the divisions.

And she says she and her department work well with other departments. 

Reeves said she is responsible for the annual financial reporting of over $30 million in federal grants awarded to the county. The department also tracks auditing, and reporting of the more than $65 million in capital assets.

She said her experience also includes records preservation and retention, and knowledge of state laws, county policies and procedures. 

Reeves said election machines undergo hours of testing to ensure accuracy. She notes that during the June 8 primary there was a recount for District 5 of the Public Service Commission race and the recount perfectly matched the earlier tally.

She said it showed the integrity and the accuracy is there.

Reeves said the office has been hit with an increase in public record requests regarding election integrity, not only from a group in Helena and other Montanans, but from across the country.

There will be a public test at 9 a.m. Nov. 4, which is the Friday before the Nov. 8 election day.

She said Lewis and Clark County in the past has served as a model county that the state points to. She wants that to continue. She said the county recently won its 26th Award of Excellence from the Government Finance Officers Association.

Starr, 72, was not immediately available for an interview for this story, but in her election materials she says she wants to see the office serve the public more effectively, more efficiently and with more transparency.

She said she has ideas for improvements for the office, including the Motor Vehicle Department and employee satisfaction. She said she is hearing from voters that they want change and that comes with new ideas.

Starr also said she wants to mitigate staff turnover and find ways to retain experienced personnel.

On her Facebook page, Starr said she opposed the county commission's decision to force mandatory mail-in ballots in 2020, saying such ballots are known to open the door to fraud. She said she also opposes using tabulating machines that aren’t secure, such as the DS850 now used in the county.

In an August email exchange with a local resident who provided the emails to the Independent Record, Starr was asked if she believed the 2020 election was stolen from Donald Trump.

"My answer is yes to your question," she said in her Aug. 3 response. "And from we the people also."

Poynter reported in June that nearly 70% of Republicans don’t see Democrat Joe Biden as the legitimate winner of the 2020 presidential election.

The IR asked her in emails earlier this month if she still thought the election was stolen.

She said in her emailed responses she believed that the 2020 election is behind us and not a concern for today, however every American should be concerned with integrity in our voting machines and everything that goes on.

“I'm not an election denier, I believe that what happened, happened," she said. " ... It's for everyone to look at.”

"I know there are a lot of irregularities that should make all of us want to make sure that everything is transparent and leaves no room for questions," she wrote, adding "it doesn't matter your political views, we should all demand clean elections."

Starr said machines don't deliver people that right to know and “I think that takes away the integrity of the election and needs to be looked at, that's all I'm saying.”

Assistant editor Phil Drake can be reached at 406-231-9021.

Mon, 17 Oct 2022 07:21:00 -0500 en text/html
Killexams : A new ‘common sense’ test for AI could lead to smarter machines

Content provided by IBM and TNW.

Today’s AI systems are quickly evolving to become humans’ new best friend. We now have AIs that can concoct award-winning whiskey, write poetry, and help doctors perform extremely precise surgical operations. But one thing they can’t do — which is, on the surface, far simpler than all those other things — is use common sense.

Common sense is different from intelligence in that it is usually something innate and natural to humans that helps them navigate daily life, and cannot really be taught. In 1906, philosopher G. K. Chesterton wrote that “common sense is a wild thing, savage, and beyond rules.”

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Robots, of course, run on algorithms that are just that: rules.

So no, robots can’t use common sense — yet. But thanks to current efforts in the field, we can now measure an AI’s core psychological reasoning ability, bringing us one step closer.

So why does it matter if we teach AI common sense?

Really it comes down to the fact that common sense will make AI better at helping us solve real-world issues. Many argue that AI-driven solutions designed for complex problems, like diagnosing Covid-19 treatments for example, often fail, as the system can’t readily adapt to a real-world situation where the problems are unpredictable, vague, and not defined by rules.

Common sense includes not only social abilities and reasoning but also a “naive sense of physics.”

Injecting common sense into AI could mean big things for humans; better customer service, where a robot can actually assist a disgruntled customer beyond sending them into an endless “Choose from the following options” loop. It can make autonomous cars react better to unexpected roadway incidences. It can even help the military draw life-or-death information from intelligence.

So why haven’t scientists been able to crack the common sense code thus far?

Called the “dark matter of AI”, common sense is both crucial to AI’s future development and, thus far, elusive. Equipping computers with common sense has actually been a goal of computer science since the field’s very start; in 1958, pioneering computer scientist John McCarthy published a paper titled “Programs with common sense” which looked at how logic could be used as a method of representing information in computer memory. But we’ve not moved much closer to making it a reality since.

Common sense includes not only social abilities and reasoning but also a “naive sense of physics” — this means that we know certain things about physics without having to work through physics equations, like why you shouldn’t put a bowling ball on a slanted surface. It also includes basic knowledge of abstract things like time and space, which lets us plan, estimate, and organize. “It’s knowledge that you ought to have,” says Michael Witbrock, AI researcher at the University of Auckland.

All this means that common sense is not one precise thing, and therefore cannot be easily defined by rules.

Secret AGENT

We’ve established that common sense requires a computer to infer things based on complex, real-world situations — something that comes easily to humans, and starts to form since infancy.

Computer scientists are making (slow) but steady progress toward building AI agents that can infer mental states, predict future actions, and work with humans. But in order to see how close we actually are, we first need a rigorous benchmark for evaluating an AI’s “common sense,” or its psychological reasoning ability.

Researchers from IBM, MIT, and Harvard have created just that: AGENT, which stands for Action-Goal-Efficiency-coNstraint-uTility. After testing and validation, this benchmark is shown to be able to evaluate the core psychological reasoning ability of an AI model. This means it can actually deliver a sense of social awareness and could interact with humans in real-world settings.

To demonstrate common sense, an AI model must have built-in representations of how humans plan.

So what is AGENT? AGENT is a large-scale dataset of 3D animations inspired by experiments that study cognitive development in kids. The animations depict someone interacting with different objects under different physical constraints. According to IBM:

“The videos comprise distinct trials, each of which includes one or more ‘familiarization’ videos of an agent’s typical behavior in a certain physical environment, paired with ‘test’ videos of the same agent’s behavior in a new environment, which are labeled as either ‘expected’ or ‘surprising,’ given the behavior of the agent in the corresponding familiarization videos.”

A model must then judge how surprising the agent’s behaviors in the ‘test’ videos are, based on the actions it learned in the ‘familiarization’ videos. Using the AGENT benchmark, that model is then validated against large-scale human-rating trials, where humans rated the ‘surprising’ ‘test’ videos as more surprising than the ‘expected’ test videos.

Common sense?

IBM’s trial shows that to demonstrate common sense, an AI model must have built-in representations of how humans plan. This means combining both a basic sense of physics and ‘cost-reward trade-offs’, which means an understanding of how humans take actions “based on utility, trading off the rewards of its goal against the costs of reaching it.”

While not yet perfect, the findings show AGENT is a promising diagnostic tool for developing and evaluating common sense in AI, something IBM is also working on. It also shows that we can utilize similar traditional developmental psychology methods to those used to teach human children how objects and ideas relate.

In the future, this could help significantly reduce the need for training in these models allowing businesses to save on computing energy, time, and money.

Robots don’t understand human consciousness yet — but with the development of benchmarking tools like AGENT, we’ll be able to measure how close we’re getting.

Sat, 01 Oct 2022 10:50:00 -0500 en text/html
Killexams : Grow your skillset: how you can advance your career with a professional certificate

After leaving school, Kevin Curtis spent 20 years working as a call operator in a security operations centre. It’s a job he’d still be doing now if he hadn’t had his interest piqued in building websites after starting a blog. He took an online course in coding to find out more – and that, he says, “sparked an interest which eventually became an interest in data analytics, machine learning and artificial intelligence”.

This intrigue eventually led to a career change. Curtis now works as a data entry coordinator for a corporate investment firm, a new direction he sought after studying for the IBM Data Science Professional Certificate on Coursera. “The programme goes through the process of getting information, gathering, preparing and then visualising data and putting it through basic machine learning algorithms,” he says.

For those like Curtis who don’t have many formal qualifications, Coursera offers the opportunity to acquire the in-demand business skills needed to change careers. With 5,200 courses available, there’s plenty of choice. They range from introductory courses for beginners to bachelor’s and master’s degrees from world-class universities – and everything in between.

Anyone who wants to find out more about online learning has the option to take one of the many free courses on offer, but many choose, as Curtis did, to work towards completing a Professional Certificate programme – a course typically lasting a few months, in which learners can build job-specific skills such as project management, digital marketing and cybersecurity. Professional Certificates, offered in partnership with global businesses, such as the Google Data Analytics Professional Certificate, IBM Data Science Professional Certificate and Meta Social Media Marketing Professional, are highly valued by employers and can help you gain new skills that enable you to switch careers.

Although students typically spend about eight hours a week in study, a principal attraction of Coursera is that students can work at their own pace. Curtis has now taken dozens of Coursera programmes, including the IBM Applied AI Professional Certificate. The suggested length of time for study was six months, but he completed it in just one. “I just fly through those things, especially when you have some knowledge already on those areas,” he says.

Learning is asynchronous (you don’t have to attend a lecture or seminar at a set time), and study materials are typically a mix of short videos and set texts, with revision quizzes to test knowledge at the end of each module. You can, however, ask questions to tutors or join an online discussion forum with other students.

Students can pay for each course individually, but Curtis chose the Coursera Plus option, which, for an annual subscription of £329, provides unlimited access to more than 90% of the learning programmes. Whenever he wants to learn a new skill for work, he turns to Coursera. “The annual subscription for me is brilliant because I dip in and out of things all the time. It’s a huge catalogue of different skills.”

Students can study in their own time, using videos and set texts. Photograph: Tom Werner/Getty Images

Like Curtis, John Guinn doesn’t have a bachelor’s degree, but has used Coursera as a way to acquire and expand his professional skills. Guinn started his career as a telecoms engineer before setting up a travel agency. A weekly gig as a travel expert on a community radio show led to an interest in journalism, and he took a master’s degree in online journalism with a university – although this was not done through Coursera. He now works as a local news journalist, reporting on the activities of local authorities.

For Guinn, Coursera has offered him an easily accessible way of gaining the skills he needs to enable him to Excellerate at his job. He has taken seven courses so far, including a short course on scepticism, offered by the University of California, to Excellerate his ability to ask thoughtful questions. “Although my online journalism master’s taught me how to do online journalism, it didn’t teach me how to be a journalist,” he says.

Another Coursera course where he enriched his knowledge, and hence his questioning skills, was the Act on Climate: Steps to Individual, Community, and Political Action course, run by the University of Michigan. This has enabled Guinn to challenge local authorities directly about their policies relating to climate change.

The other courses he’s studied, more technical in nature, such as Visualization for Data Journalism, have taught him how to analyse and visualise data. These have been invaluable in enabling him to spot information tucked away in the spreadsheets that local authorities are legally required to publish. Close analyses can generate important local news stories by revealing information that a local authority is reluctant to publicise. It has enabled Guinn to spot, for example, that, despite making public pronouncements about the importance of recycling, one council’s recycling rate has gone down while its incineration rate has gone up.

Guinn plans to continue taking Coursera courses to sharpen his journalism and data analysis skills and, like Curtis, has signed up for an annual Coursera Plus subscription. He hopes these skills will help him move into more in-depth investigative journalism, focusing, in particular, on climate change.

Both Guinn and Curtis have found that Coursera courses can be life-transforming, contributing to their current positions. “Based on the value I’ve got from it, I’d absolutely recommend it,” says Curtis.

Whether you’re at the beginning of your career journey or looking to enhance your skillset to make a mid-career transition, you can choose from a range of learning experiences on Coursera to find the programmes that are right for you.

Thu, 06 Oct 2022 22:08:00 -0500 Kim Thomas en text/html;amp
Killexams : Test Data Management Market Share 2022 Key Opportunities, Regional Segments, Business Statistics, Development, Size and Growth Forecast to 2029

The MarketWatch News Department was not involved in the creation of this content.

Oct 04, 2022 (The Expresswire) -- Global “Test Data Management Market” Report 2022-2029 discusses an innovative concept of top growing business strategies, development trends, major key players with key dynamics. It provides in-depth and comprehensive analysis of overall growth statistics, business prospects, latest opportunities and challenges. Test Data Management market report gives analytical overview of key companies, business profiles, investment plans, CAGR status, industry revenue estimations and SWOT analysis. It also sheds light on demand scope, future trends, financial overview, production and consumption analysis, and segmentation at global and regional level.

Test Data Management Market has witnessed a growth from USD million from 2017 to 2022 with a highest CAGR is estimated to reach USD in 2029.

Get a demo PDF of the report at -

Test data management is the process of planning, designing, storing and managing software quality-testing processes and methodologies. It allows the software quality and testing team to have control over the data, files, rules, and policies produced during the entire software-testing life cycle.

The report focuses on the Test Data Management market size, segment size (mainly covering product type, application, and geography), competitor landscape, accurate status, and development trends. Furthermore, the report provides detailed cost analysis, supply chain. Technological innovation and advancement will further optimize the performance of the product, making it more widely used in downstream applications. Moreover, Consumer behavior analysis and market dynamics (drivers, restraints, opportunities) provides crucial information for knowing the Test Data Management market.

Get a demo PDF of the Test Data Management Market Report

The report can help to understand the market and strategize for business expansion accordingly. In the strategy analysis, it gives insights from marketing channel and market positioning to potential growth strategies, providing in-depth analysis for new entrants or exists competitors in the Test Data Management industry.

The report covers extensive analysis of the key market players in the market, along with their business overview, expansion plans, and strategies. The key players studied in the report include:

● Innovative Routines International ● Compuware ● CA Technologies ● Ekobit ● IBM ● MENTIS ● Solix Technologies ● Informatica ● Cigniti Technologies ● Delphix Corporation ● Infosys ● DATPROF ● Original Software Group

Based on types, the Test Data Management market from 2017 to 2029 is primarily split into:

● Implementation ● Consulting ● Support and Maintenance

Based on applications, the Test Data Management market from 2017 to 2029 covers:

● Data Subsetting ● Data Masking ● Data Profiling and Analysis ● Data Compliance and Security ● Synthetic Test Data Generation ● Others

Inquire or Share Your Questions If Any Before the Purchasing This Report -

The report also tracks the latest market dynamics, such as driving factors, restraining factors, and industry news like mergers, acquisitions, and investments. It provides market size (value and volume), market share, growth rate by types, applications, and combines both qualitative and quantitative methods to make micro and macro forecasts in different regions or countries.

Major Regions or countries covered in this report:

● United States ● Europe ● China ● Japan ● India ● Southeast Asia ● Latin America ● Middle East and Africa ● Others

Some of the Key Questions Answered in this Report:

● What is the Test Data Management market size at the regional and country-level? ● What are the key drivers, restraints, opportunities, and challenges of the Test Data Management market, and how they are expected to impact the market? ● What is the global (North America, Europe, Asia-Pacific, South America, Middle East and Africa) sales value, production value, consumption value, import and export of Test Data Management? ● Who are the global key manufacturers of the Test Data Management Industry? How is their operating situation (capacity, production, sales, price, cost, gross, and revenue)? ● What are the Test Data Management market opportunities and threats faced by the vendors in the global Test Data Management Industry? ● Which application/end-user or product type may seek incremental growth prospects? What is the market share of each type and application? ● What focused approach and constraints are holding the Test Data Management market? ● What are the different sales, marketing, and distribution channels in the global industry? ● What are the upstream raw materials and manufacturing equipment of Test Data Management along with the manufacturing process of Test Data Management? ● What are the key market trends impacting the growth of the Test Data Management market? ● Economic impact on the Test Data Management industry and development trend of the Test Data Management industry.

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Following Chapter Covered in the Test Data Management Market Research:

Chapter 1 provides an overview of Test Data Management market, containing global revenue and CAGR. The forecast and analysis of Test Data Management market by type, application, and region are also presented in this chapter.

Chapter 2 is about the market landscape and major players. It provides competitive situation and market concentration status along with the basic information of these players.

Chapter 3 introduces the industrial chain of Test Data Management. Industrial chain analysis, raw material (suppliers, price, supply and demand, market concentration rate) and downstream buyers are analyzed in this chapter.

Chapter 4 concentrates on manufacturing analysis, including cost structure analysis and process analysis, making up a comprehensive analysis of manufacturing cost.

Chapter 5 provides clear insights into market dynamics, the influence of COVID-19 in Test Data Management industry, consumer behavior analysis.

Chapter 6 provides a full-scale analysis of major players in Test Data Management industry. The basic information, as well as the profiles, applications and specifications of products market performance along with Business Overview are offered.

Chapter 7 pays attention to the sales, revenue, price and gross margin of Test Data Management in markets of different regions. The analysis on sales, revenue, price and gross margin of the global market is covered in this part.

Chapter 8 gives a worldwide view of Test Data Management market. It includes sales, revenue, price, market share and the growth rate by type.

Chapter 9 focuses on the application of Test Data Management, by analyzing the consumption and its growth rate of each application.

Chapter 10 prospects the whole Test Data Management market, including the global sales and revenue forecast, regional forecast. It also foresees the Test Data Management market by type and application.

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Detailed TOC of Test Data Management Market Forecast Report 2022-2029:

1 Test Data Management Market Overview

1.1 Product Overview and Scope of Test Data Management

1.2 Segment by Type

1.3 Global Segment by Application

1.4 Global Market, Region Wise (2017-2022)

1.5 Global Market Size of Test Data Management (2017-2029)

2 Global Test Data Management Market Landscape by Player

2.1 Global Test Data Management Sales and Share by Player (2017-2022)

2.2 Global Revenue and Market Share by Player (2017-2022)

2.3 Global Average Price by Player (2017-2022)

2.4 Global Gross Margin by Player (2017-2022)

2.5 Manufacturing Base Distribution, Sales Area and Product Type by Player

2.6 Market Competitive Situation and Trends

3 Test Data Management Upstream and Downstream Analysis

3.1 Industrial Chain Analysis

3.2 Key Raw Materials Suppliers and Price Analysis

3.3 Key Raw Materials Supply and Demand Analysis

3.4 Manufacturing Process Analysis

3.5 Market Concentration Rate of Raw Materials

3.6 Downstream Buyers

3.7 Value Chain Status Under COVID-19

4 Test Data Management Manufacturing Cost Analysis

4.1 Manufacturing Cost Structure Analysis

4.2 Test Data Management Key Raw Materials Cost Analysis

4.3 Labor Cost Analysis

4.4 Energy Costs Analysis

4.5 RandD Costs Analysis

5 Market Dynamics

5.1 Drivers

5.2 Restraints and Challenges

5.3 Opportunities

5.3.1 Advances in Innovation and Technology for Test Data Management

5.3.2 Increased Demand in Emerging Markets

5.4 Test Data Management Industry Development Trends under COVID-19 Outbreak

5.4.1 Global COVID-19 Status Overview

5.4.2 Influence of COVID-19 Outbreak on Test Data Management Industry Development

5.5 Consumer Behavior Analysis

6 Players Profiles

6.1 Company A

6.1.1 Basic Information, Manufacturing Base, Sales Area and Competitors

6.1.2 Test Data Management Product Profiles, Application and Specification

6.1.3 Test Data Management Market Performance (2017-2022)

6.1.4 Business Overview

6.2 Company B

6.2.1 Basic Information, Manufacturing Base, Sales Area and Competitors

6.2.2 Test Data Management Product Profiles, Application and Specification

6.2.3 Test Data Management Market Performance (2017-2022)

6.2.4 Business Overview

7 Global Test Data Management Sales and Revenue Region Wise (2017-2022)

7.1 Global Sales and Market Share, Region Wise (2017-2022)

7.2 Global Revenue (Revenue) and Market Share, Region Wise (2017-2022)

7.3 Global Sales, Revenue, Price and Gross Margin (2017-2022)

8 Global Test Data Management Sales, Revenue (Revenue), Price Trend by Type

8.1 Global Sales and Market Share by Type (2017-2022)

8.2 Global Revenue and Market Share by Type (2017-2022)

8.3 Global Price by Type (2017-2022)

8.4 Global Sales Growth Rate by Type (2017-2022)

9 Global Test Data Management Market Analysis by Application

9.1 Global Consumption and Market Share by Application (2017-2022)

9.2 Global Consumption Growth Rate by Application (2017-2022)

10 Global Test Data Management Market Forecast (2022-2029)

10.1 Global Sales, Revenue Forecast (2022-2029)

10.2 Global Sales and Revenue Forecast, Region Wise (2022-2029)

10.3 Global Sales, Revenue and Price Forecast by Type (2022-2029)

10.4 Global Consumption Forecast by Application (2022-2029)

10.5 Test Data Management Market Forecast Under COVID-19

11 Research Findings and Conclusion

12 Appendix

12.1 Methodology

12.2 Research Data Source

For Detailed TOC -

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Mon, 03 Oct 2022 16:51:00 -0500 en-US text/html
Killexams : Career as Data Analyst: Scope, online courses and responsibilities Career as Data Analyst: Scope, online courses and responsibilities Career as Data Analyst: Scope, online courses and responsibilities

There's no denying that data is the new gold. With the rise in technology, collection of useful data has become possible. But what's the best way to collect this data? How do organisations make sense of it? This is where a Data Analyst steps in. A Data Analyst doesn't only collect the data but also analyse it by using advanced statistical tools.

Due to the pandemic, most businesses have started online activities and the demand for data analysis has skyrocketed. Data Analysts help organisations understand how raw data can be used to find solutions for some of the most challenging questions.

If you are looking to build a career in the field of data professionals, now is the time. A study conducted by IBM and Burning Glass Insights showed that in 2020 alone there were 2.7 million job openings for data professionals.

This goes to say that a career in the field of the data profession has grown and is only growing.

Let's look at the Indian job market. According to a study by AIMResearch, close to about 93,500 new job openings were available by the end of August 2020.

This sharp rise in the job openings is testament to what an important role a data Analyst plays in any organisation. Needless to say, the role of a data Analyst cuts across all divisions of an organisation. For example, in the field of marketing, a data Analyst may help brands tap into new audiences. Likewise, in the case of the manufacturing department, a data analyst will be able to find out the reason for delays or spillage, so the right management decisions should be made.

Responsibilities of a Data Analyst

By now you know the crucial role that a Data Analyst plays in any organisation, which makes it a dream job for many. Next, you must know all the responsibilities and challenges that may come your way in the job.

1. Reporting

One of the main responsibilities of a Data Analyst is to maintain timely reports. These reports essentially deliver the major business insights to the top management on which they can make necessary decisions. Now these reports need to weave a story, they need to be crisp and they must answer all the questions that the decision-making team may have.

2. Collaboration

While it may appear as an independent department, this isn't the case. It is important for a Data Analyst to collaborate with teams. Good collaboration ensures proper data collection, one may collaborate with campaign managers, sales people, operations executives etc. Another level of collaboration occurs within the data professional teams, to ensure smooth functioning.

3. Setting processes

The duties of a Data Analyst are vast. It starts from collecting the data and ends by drawing conclusions. One must be able to set up seamless processes to understand this complex world of data. The complexity doesn't just end in the vastness of data collection, but also remains in the fact that there are multiple teams with multiple requirements.

Learn on your own

If you are just starting out your career as a Data Analyst, you need to gather knowledge. This job requires analytical skills and the use of some advanced tools. If you have decided to learn on your own, start by making a list of tools that you must learn. Now, understand that there isn't a 'must know' tool for every data analyst. Some tools that are useful include - Microsoft Excel, SQL, SAS software, Google Analytics. Start by memorizing up on how to use them, followed by testing them with a hypothetical set of data.

Another way to learn on your own is by memorizing books written on the subject. Some of the best in the field are; Data Analytics Made Accessible by Dr Anil Maheshwari, Python for Everybody: Exploring Data in Python 3 by Dr Charles Russell Severance.

Once you have garnered the required skills and knowledge, take up small projects to build your portfolio and test what you learnt. This will help you come closer to what actual work as a Data Analyst would be like.

Specialised online course

Another great way of gaining all the knowledge is by doing a specialised online course. There are many crash courses perfectly designed for some starting fresh and making a quick shift to Data Career. Here's a list of advantages of taking an online course:

  • Well researched and thoroughly planned curriculum.This saves a lot of your time which otherwise would have gone to researching the best memorizing material, finding the right books, or planning your study days.
  • Acclaimed faculty. While in the case of self study, you may have to read a variety of different books to be able to learn from different individuals. Coding Invaders does this job for you, because the course is taught by trainers working in the pioneer organisations like Google, Microsoft and Facebook.
  • Real life tasks. That is something that makes Coding Invaders stand out is the simulation training provided during the course. These simulations are based on real life case studies which deliver you an upper hand when looking for a job.
  • Ask about the percentage of placement students? If the education platform hides these numbers, then run away from it.
  • Lastly, you don't only get amazing trainers but also become a part of a close group of other students. Here you can collaborate and learn from each other. This isn't possible when you are learning on your own,
  • Ask somebody who has passed the course and gets a placement. Ask them about your fears? Tell him your story and ask if the course is suitable for you personally.

A career in the field of Data Analysis can be very beneficial. Especially in the times where data plays the most important role in every business decision. Taking this leap may be scary, but the fruits of your labour will indeed be sweet. So take the leap and we will be here to help you.

Read: 5 job portals to help you find the right job in 2022

Read: Career opportunities in blockchain that you can consider in digital world

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Thu, 09 Dec 2021 14:33:34 -0600 en-IN text/html
Killexams : Temple Grandin Is a Visual Thinker Who Hates Graphic Novels No result found, try new keyword!It is too difficult for me to constantly switch back and forth between the pictures and the text bubbles,” says the animal behaviorist and advocate for autistic people, whose new book (with Betsy ... Thu, 06 Oct 2022 00:46:00 -0500 text/html
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