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Foreign Operatives Allegedly Using Zoom To Spy On Americans – Brinkwire

The COVID-19 pandemic triggered the increase in the use of video chatting and teleconferencing apps like Zoom. These kind of apps become popular because businesses shut down to help flatten the curve and arranged awork-from-home set-up. However, U.S. officials have discovered that these apps are being used by foreign operatives to spy on Americans.

A recent article from theTimerevealed an alarming discovery from the US government. According to the report, three U.S. counter-intelligence agencies found out that foreign operatives are using Zoom to spy on Americans. These foreign cyberspies are Russians and Chinese, according to the report. One of the three U.S. officials revealedthat more than anyone else, the Chinese are interested in what American companies are doing, implying that Chinese operatives are the most aggressive ones when it comes to spying.

With millions of Americans now using Zoom for their work from home needs, internet security researchers and intelligence officials are increasingly alarmed. On Apr. 3, The Citizen Lab, a research team of the University of Toronto, revealed thatit had found several security issues with Zoom. One of these issues allows users to be defenseless to China.

The report stated that Zooms encryption keys, via its Chinese servers, are responsive to pressure from Chinese authorities. The Chinese servers are also weak, and the apps ownership is dependent on Chinese labor. Additionally, U.S.intelligence officials revealed that all conferences in the platform have end-to-end encryption.

Zoom made a swift response to address the issue by releasing a series of public statements. It also denied the allegations about the end-to-end encryption and claimed that the online messaging tools of the app do not have one. It is worth noting, however, that, while the U.S. intelligence officials have discovered this kind of observation in Zoom, it clarified that, at this point, they were not able to piece any evidence that shows the popular video conferencing apps is working with China.

Earlier this week, the New York City Department of Education banned students and teachers from using Zoom. It also recommended switching to Microsoft Teams as the new video teleconferencing platform as soon as possible over privacy and security concerns.Zoom recently said that it was overwhelmed by the increased number of users of its platform but assured the public that it is doing its best to further improve the privacy and security of the app.

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Top Ways to Guard Against Work-from-Home Phishing Threats – Infosecurity Magazine

By now were all familiar with what social distancing means as it relates to stopping or slowing down the spread of the coronavirus. Following what have rapidly become best practices along with government the mandates many companies are practicing social distancing by encouraging or requiring employees to work from home.

While thats a sensible approach on many levels, it does present potential cybersecurity problems, as workers move from trusted and secured office networks to remote locations, taking advantage of at-home internet connectivity and power sources, but often falling short when it comes to security coverage. Its a move that extends corporate networks in ways that make them more difficult to secure, providing cyber criminals with an almost irresistible opportunity to take advantage of the situation.

In fact, the National Cyber Security Centre (NCSC) has issued a warning that criminals are looking to exploit the spread of coronavirus to conduct cyber attacks and hacking campaigns. NCSC experts have seen multiple scams and cyber threats that look to take advantage of COVID-19 for their own malicious ends.

Cyber-criminals are already using "Coronavirus" and COVID-19 as subject lines for phishing scams, hoping to fool unsuspecting workers into clicking on a link or opening an attachment that results in the installation of malware or unwittingly handing over usernames and passwords.

With that in mind, here are six best practices that can help raise awareness of potential phishing techniques and other scams, and help keep your systems and data safe while you and your employees work from home:

At a time when we all have so much on our minds, following these recommendations can help keep you and your companys data safe from cyber-attacks as you keep yourself and your loved ones safe while working outside of the office. At a time of great distraction like this, individuals are more likely to slip up and be a victim of phishing.

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COVID-19 will accelerate AI’s replacement of humans as factor of production | TheHill – The Hill

As the world speeds toward an unprecedented economicrecessionwith billions of people and businesses across the planet under some form of lockdown for weeks or months amid one of the worst pandemics on record,countingmore than 1.8 million people infected and more than 100,000 dead so far, the novel coronavirus will likely produce a yet more enduring change in a post-COVID-19 world: the replacement of humans as factor of production.

Prior to COVID-19, the often-contested race between human productivity options and machine productivity advantages appeared less definite. In the last two decades and with aim of making humans highly productive, promoting general wellbeing and increasing business competitiveness, several ideas gained traction namely,telecommuting,remote work, andcoworking spaces. In fact, billions of dollars have been invested around the world inreal estate (co-working spaces) to enable people to commute to their workplace by either just taking an elevator or walking a few blocks from home. Yet the very nature of the COVID-19 virus imposing social distancing as precautionary measure has made remote work our last available option to move on with our lives.

Prior to COVID-19, moreover, investment in artificial intelligence (AI) was already on the rise, going from $12 billion in2017to projected $60 billion in2021. These figures dont count privateinvestments in the more controversial intelligence enhancement(IE). Instead, this trend reflects massive investments in current artificial intelligence (machine and deep learning)and, gradually, major investments in future AI quantum artificial intelligence(QAI) and quantum computing technology inUnited States, China, Canada, Japan,India,Germany andRussia.

Make no mistake, as companies are losing billions of dollars across the globe, COVID-19 will be the triggering event leading to the acceleration of AIs replacement of humans as factor of production.

Most governments, international organizations, companies, and universities have now transitioned to remote work. And by now, people interacting within these organizations have learned about the restrictions and, quite likely, the psychological effects such limited interaction produces. In fact, notwithstandingdirect benefits(less commuting time, stress reduction, creativity boost) andincidental advantagesfound in remote work (reduction of environmental, operative, healthcare, and liability costs) research on thepsychological impactof remote work indicates thatisolation,depression,passive leadership, andlack of communication(decreasing teamworkefforts) are some of the most consequential effects.

Despite existential, human rights, and socioeconomicconcernsabout automation, the premise promoting an automated world is rather simple and, in many ways, unavoidable. That is, unlike humans, machines do not get sick and, thus, will not stop production particularly when this one is needed the most. In the midst of a pandemic and in a fully automated world, machines could provide humans with a much-needed uninterrupted chain of food supply and healthcare-critical equipment (tests, ventilators, masks, gloves, hospital beds) as well asautomatedandremotely supervisedmedical attention with minimal or no risk of infection to medical workers. The benefits both in human lives saved and in uninterrupted economic activity are, at least in the short term, indisputable.

Still, the effects of this replacement in the long term and in relation to human interaction, functionality, and purpose are far more complex. What would we do with the millions of humans who will nottransitiontowards other creative, highly-specialized, or managerial tasks? How could a fully automated world assure continued consumption, human dignity and sources of revenue to all its inhabitants as well as socioeconomic equality among people and countries? Unfortunately, these concerns are unlikely to be at the forefront of the upcoming-accelerated automation process. After all, the decision-making process of modern humans, their political systems, and their leaders are not characterized by their long-term perspective.

In the post-COVID-19 world, the numbers reflecting the pandemic and its economic impact will likely become the justification for a shift of paradigm in human production.

Making determinations about our future during the most uncertain present will thus require not merely competence but wisdom. Return to normalcy is a self-deception.

J.Mauricio Gaona is a researcher at the Institute for Global Law and Policy at Harvard University, visiting researcher at Harvard Law School, and O'Brien Fellow at McGill's Faculty of Law (Center for Human Rights).

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IoT news of the week for April 10, 2020 – Stacey on IoT

Cisco acquires Fluidmesh for super-fast IoT networks:This week, Cisco said it had acquired New York-based Fluidmesh Networks, which makes a wireless backhaul technology that can deliver packets from devices moving at more than 300 km/h (186 mph). This makes the technology especially useful for tracking sensor data from trains, mining carts, and ships. While Cisco has been a leader in networking, it hasnt spent much time acquiring actual wireless network assets, signaling perhaps that the IoT in particular the industrial IoT needs specialized networking tech and that Cisco is willing to provide it. (Cisco) Stacey Higginbotham

Quantum security for the IoT: Im still feeling a little doubtful about the looming threat of quantum computers to our traditional cryptographic methods, but this article lays out several projectsto address the threat that such computers pose. The fear is that quantum computers could quickly and easily break traditional AES security, suddenly making it simple to obliterate the most difficult and common cryptographic security we currently have. Thus, chipmakers and computer scientists are trying to figure out alternatives for a world where quantum computing is more powerful and commonplace than it is today. Todays quantum computers are not quite powerful enough, and are so expensive and persnickety that they are held in large corporate and government labs, which makes the threat to traditional security low. But you can never be too sure. (EETimes) Stacey Higginbotham

Openpath offers up data on quarantine compliance: While Google is sharing anonymized location data from phones to track peoples movements during thequarantine, other companies are trying to see if they can provide similar insights. Openpath, which makes smart access products for offices, has released data showing how many people are coming into work. The idea is that someone can look at the data across states and industries and get a feel for how well a quarantine is working, along with which businesses might be essential. In most places, the data shows a steep drop-off from the end of February to now, with the exception of states such as Nebraska, which have yet to issue any orders for people to stay home. From an industry perspective, its clear that the government is still working, as are many manufacturing plants.(Openpath) Stacey Higginbotham

Why yes, I would like this smart kitchen in my near future: My favorite exhibits at CES outside of chip vendors booths, where I can find all kinds of goodies are the appliance makers future kitchen displays. As a consumer and a cook, I love seeing what craziness the kitchen has in store for me. Maybe its a connected vent hood with a display that can share recipes and see what Im cooking on the stove below. Maybe its an inductive countertop that powers my blender or can even heatupspecialty cookware. Im here for all of it. Which is why I loved reading this article, which tries to showcase how grocers and food delivery services could integrate into the digital kitchen of the near future. (Progressive Grocer) Stacey Higginbotham

Amber wants to update our electrical devices: This week, I spoke with a startup making a system on a chip that eliminates much of the bulk and infrastructure designed to protect appliances from electrical surges. The breakthrough could lead to electrical products (e.g., switches, outlets, and circuit breakers) that are slimmer and have more features than they do now.Well see. (StaceyonIoT) Stacey Higginbotham

Ring Home Alarm gets a second-gen refresh:Ring is now taking pre-orders for its updated Home Alarm security products, which will begin shipping onApril 29. Prices start at $199.99 for the basic, 5-piece kit, which includes a multi-radio base station, keypad, range extender, motion sensor, and contact sensor. The main difference I see is sleeker-looking products, including a smaller, square keypad that replaces the older, rectangular one. This keypad also has three dedicated buttons to contact police, fire, or emergency medical services in your area. (Ring) Kevin C. Tofel

Google wants to make it easier to link smart home device accounts: The big smart home news out of Google this week was about the Local Actions SDK coming out of preview for developers. But as I dug around a little more on the Google Developers site, I noticed something else coming soon in preview, called App Flip. The idea is to make the linking of third-party IoT apps and products to Google Home faster and easier, without a lot of back and forth between different apps. Color me intrigued. (Google Assistant Developer Docs) Kevin C. Tofel

I just bought a next-generation pair of hearables: After taking a hearing test on a mobile app this week, I got confirmation of what Ive essentially known for at least five years: My range of hearing is diminishing. Enter the small hearables sector of smart devices that we discuss from time to time on the podcast. For $359, I pre-ordered the Nuheara IQBuds2 Max, and should have them next month, so stay tuned for a review. These are personal sound amplification products, or PSAPs, that double as wireless Bluetooth headphones. They also provide one-touch access to the digital voice assistant on my phone. And for improved hearing ability in various environments, they can amplify specific frequencies and be tuned for specific spaces, such as a crowded room, outside, and more. Nuheara says these use the NAL-NL2 algorithm provided by Australias National Acoustics Laboratories and used by audiologists for hearing aid configuration. (Nuheara) Kevin C. Tofel

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Global Quantum Computing Market To Grow at a Stayed CAGR with Huge Profits by 2025 | D-Wave Systems Inc. (Canada) – Fashion Trends News

According to a new report published by Eon Market Research, titled, Global Quantum Computing Market by Product Type and by End-Users/Application Global Market Share, Forecast Data, In-Depth Analysis, and Detailed Overview, and Forecast, 2020 2025 By Regions (USA, Europe, Japan, China, India, South East Asia). The report has offered an all-inclusive analysis of the global Quantum Computing market taking into consideration all the crucial aspects like growth factors, constraints, market developments, top investment pockets, future prospects, and trends. At the beginning, the report focuses on key developments and opportunities that may arise in the immediate future and have a significant effect on the overall growth of the industry.

Some of the global major players operating in the Quantum Computing market include: D-Wave Systems Inc. (Canada), 1QB Information Technologies Inc. (Canada), QxBranch LLC (US), QC Ware Corp. (US), Research at Google-Google Inc. (US).

[Note: Our Free Complimentary Sample Report Accommodate a Brief Introduction To The Synopsis, TOC, List of Tables and Figures, Competitive Landscape and Geographic Segmentation, Innovation and Future Developments Based on Research Methodology]

Global Quantum Computing Market: Type analysis

SimulationOptimizationSampling

Global Quantum Computing Market: Application analysis

DefenseBanking and FinanceEnergy and PowerChemicalsHealthcare and Pharmaceuticals

Market Segment by Regions:-

USAEuropeJapanChinaIndiaSoutheast Asia

Drivers for the Global Quantum Computing Market: Quantum Computing equipment provides comfortable and flexible features which enhances their demand in market. Moreover, electric equipment manufacturer introduces some light compaction equipment which are manufactured by plastic. Hence, rising demand for light compaction electric equipment boost the market of Quantum Computing market. Further, the railway industry is adopting various advanced changes for innovative products which is also responsible for the growth of market.

Opportunities for the Global Quantum Computing Market: Due to rapid industrialization in emerging countries, increasing disposable income, and high purchasing power are likely to impact growth of the many industries. In addition, SME and large enterprises gives preference to the innovative and flexible electric products. And also, demanding for the automation solution of electric equipment from manufacturers which is creating the huge market opportunity for the Quantum Computing market.

Restrains for the Global Quantum Computing Market: The requirement for the Quantum Computing in various industries is elastic as the consumers are sensitive towards the changes in products. Competition is thus increasing day by day. In addition, consumers mind continuously changes according to the offers, usage, cost, features of the products. rising market competition create some type of negative impact on the growth of the Quantum Computing market. Further, shortage of metals is responsible for the negative growth rate of the market. It can majorly hamper heavy-duty market.

Region Wise, Global Quantum Computing Market Analysis: North America held maximum share in the market as regions like US has developed industrialization. Moreover, this region has relatively high purchasing power are likely to impact growth of the many industries. The Asia Pacific region is expected to show good growth opportunities on account of rapid industrialization and growing industrial automation major end-use industries. The European region is projected to hold a notable market share on account of the early adoption of technology and well-established infrastructure.

Do Inquiry Before Purchasing Report Here and Ask For Discount Here- https://www.eonmarketresearch.com/enquiry/53834

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‘Westworld’: Rehoboam name inspired by sci-fi book ‘Stand on Zanzibar’ – Insider – INSIDER

In a show like "Westworld," the various names of characters are likely to have a larger symbolic meaning. For example, take another look at the name Dolores. It has roots in Spanish and Latin, and can be roughly translated to mean "pain" or "sorrows." Or how Caleb's name means "dog" or "loyal follower."

So when the new artificial intelligence (AI) system Rehoboam was introduced, many people sought out an explanation for the unique name choice. We thought we found the answer in Biblical history, since Rehoboam was the son of Solomon, and both men were Israelite kings.

"Well I spent nine years in Catholic school, but I'm ashamed to say it has actually nothing to do with the biblical reference," Jonathan Nolan, cocreator of "Westworld," told Insider over the phone in a recent interview. "It's an homage to a book called 'Stand on Zanzibar,' which is a seminal piece of science fiction. It's an absolutely terrific and frightening book."

Lisa Joy and Jonathan Nolan (the wife-husband duo who cocreated "Westworld") and the latest book cover for "Stand on Zanzibar." Getty/Macmillan

Brunner's "Stand on Zanibar," which won a coveted Hugo Award for best novel, follows an executive at a company called General Technics. The synopsis describes the sci-fi world as a place "where society is squeezed into hive-living madness by god-like mega computers, mass-marketed psychedelic drugs, and mundane uses of genetic engineering."

Those themes should sound familiar to "Westworld" fans, especially with its third season.

"That was a period in science fiction when we'd got out of utopian science fiction and into much more frightening imaginings about where the world might take us," Nolan said. "And in that book there is an AI owned by the General Technics corporation called Shalmaneser."

In our real history, Shalmaneser V was a King of Assyria and Babylon in the eighth century who subjugated Israel.

Not only did "Stand on Zanzibar" spark the idea for Serac's AI system in "Westworld" to be named for historical kings, but its physical structure was also inspired in part by the Shalmaneser AI in Brunner's novel.

"Shalmaneser is literally in the lobby of the General Technics incorporation an idea that I love," Nolan said. "And that's the reason why Rehoboam is in the lobby of Incite. It has this delicious subversive idea to it that they would put this thing in full display, that they would put it right there."

"Westworld" season three, episode five, "Genre." HBO

"So much of what [companies like] Google or Facebook does is in part hidden by design, because it requires thousands of diesel generators or a hydroelectric plant," Nolan said. "All of that hardware is out of sight."

"With the advent of quantum computing and Rehoboam is a quantum computer we will come to a moment where you don't hide the hardware anymore," Nolan continued. "This is just good PR. You put it front and center and you let school kids literally walk around it, right? Because that doesn't mean f------ anything"

In the first episode of "Westworld" season three, Dolores sees Rehoboam for the first time while visiting the Incite headquarters. And indeed, just as Nolan says, there are schoolchildren in the background of that scene.

The Rehoboam system at Incite's Los Angeles office building. HBO

"You could look at this thing and say, 'Look, here it is in the lobby of our building. There's nothing to be afraid of. It's right there,'" Nolan said. "What it's doing, however, is something that only a handful of human beings might even understand."

Nolan series, which he cocreated with his wife Lisa Joy, is based on the 1973 movie of the same name, which was written and directed by Michael Crichton. Though many people may know Crichton best for his other theme-park-gone-wrong novel, "Jurassic Park," he was another sci-fi writer who dealt with early concepts of computer power.

"There's this lovely line in Crichton's original movie that we come back to again and again as a source of inspiration," Nolan said. "It's when the chief scientist in the film is trying to figure out what the f--- is going on, and he turns to one of his colleagues and says, 'In some cases, these computers have been designed by other computers and we don't even understand how they work.'"

Alan Oppenheimer played the Chief Supervisor of the Delos company in the original "Westworld" movie. MGM

"As usual with Crichton who is a deeply brilliant person, a polymath, and someone who really had his finger on the pulse of where technology could take us in both good and bad ways [he had] the idea that we would reach this inflection point where the machines are making the machines," Nolan continued. "We have only second hand control and a second hand understanding of how they even work."

This idea, again, carries over right into the HBO adaptation of "Westworld." In season three, we learned about Rehoboam and how it powered Incite a company which was dictating the lives of nearly every person on Earth without them knowing it.

Liam Dempsey, the CEO of Incite, was a mere figurehead. He confessed to Dolores that he had no idea what the system was actually doing, or how it worked. Only its creator, Serac, knew for certain.

Vincent Cassel as Enguerrand Serac in "Westworld." John P. Johnson/HBO

"What's so frightening and interesting about the moment we're in right now is we just turned off the engine of our economy," Nolan said. "And the question is going to be does anyone actually understand how to turn it back on again? Or is the machine we've built, the machine of our society, too complicated that no one knows what happens when we turn it back on? I think we're pretty clearly at that moment."

Though Nolan was referring to the current global pandemic of COVID-19, this anecdote is again paralleled in "Westworld." The latest episode, "Genre," showed Dolores wreaking havoc on the structured society Incite and Rehoboam had built. She cut the cord on the system by revealing Rehoboam's life profiles to everyone.

Like a flip switching, everything seemed to freefall. But what now? The next step in her plan isn't clear yet, and neither is Serac's counter move.

"In 'Stand on Zanzibar' things do not end well for most folks," Nolan said. "But they do [end] OK for Shalmaneser."

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Why the information security of your company depends on machine learning – SC Magazine

Machine learning operations (MLOps) technology and practices enable IT teams to deploy, monitor, manage, and govern machine learning projects in production. Much like DevOps for software, MLOps provides the tools you need to maintain dynamic machine learning-driven applications. The security of your future enterprise depends on the decisions you make today related to these new applications and the code that powers them. So, what are the risks?

Good People, Bad Code Data scientists are known for building predictive models and not for their coding skills. Taking their handwritten code and putting it straight into production is a recipe for failure and a potential security risk.

Malicious Code If someone wanted to harm your business, introducing code into your production machine learning applications would be one way to cause problems. This problem is compounded when your data science team uses a language like Python or R that your IT team doesnt understand, making it so that your IT team cannot review the code. This code could return bogus results or overload servers and create any number of issues. Malicious code is most likely to work if you dont have a proactive way to know if production models and their related artifacts are performing as expected.

Adversarial Inputs Someone is submitting requests that your machine learning model has never seen before, and it responds in a way that you dont expect. Suddenly, a process that seemed pretty solid is under attack, and your business is giving out approvals, returns, or something that costs you money or hurts your reputation.

Data Pollution or Poisoning Models are the product of data and algorithms. If the data used to train those models contains patterns that are unknown to you but favorable to someone outside your business, that could be bad for you. In the case of spam filtering, for example, hackers could report a bunch of items that are not really spam to your spam detection system. This could dilute the effectiveness of your spam detection model, resulting in more spam or specific spam getting through.

Denial of Service Attack on ML Endpoints All machine learning platforms are not created equal. Many data science teams deploy their own production endpoints in front of their models and try to use those to support production business applications. Unfortunately, the servers powering these endpoints were built for experimentation and validation and not for real production use. Therefore, when they start to see a load, they cant scale, and your business application starts to fail. If hackers find these weak endpoints, they can shut them down or slow them down with some fake traffic.

Model Theft Your business has paid a lot to develop machine learning models, including hiring data scientists, purchasing data science platforms, and building out specific AI infrastructure. AI and machine learning create a competitive advantage for your business. As such, they are particularly desirable assets for theft, most likely from people within your organization who are leaving for a new job. You need to make sure you have tight controls on model access.

MLOps and InfoSec

Machine learning operations technology and practices can mitigate security issues with machine learning models and applications. Heres how:

Production Coding Practices Production coding best practices are critical for all software projects, including machine learning models. Your data scientists are not developers. As the first line of defense, you should pair a data scientist with a production developer when developing production models. Consider providing training for your data science teams on production coding best practices. Having people on your IT team that understand the languages your data science team is using is also a good idea, as is testing your machine learning code. As you move towards production, you should have a set of tests you can run to ensure your machine learning models are performing as you would expect. Having safeguards in place for your production machine learning projects is also important, like having the ability to version control the code and roll back when you encounter issues.

Shadow/Warmup for Model Updates Models should not be turned on in production without extensive testing under production conditions. Model updates should be deployed in a shadow mode on production environments without providing results to your endpoint. The results and service performance should be logged for analysis. This warmup period allows the operator to see that the model is behaving as expected before replacing the production model with the updates.

Production Endpoints Production machine learning requires production endpoints that can scale with production needs. This includes running the endpoints on production servers that leverage technologies like Kubernetes and autoscaling to ensure that the services can scale up as load increases.

Data Drift and Anomaly Detection Your machine learning models are trained on a profile of data. When a request comes in that does not fit the profile of data you trained on, that could indicate an issue. When this change is to the overall pattern of the data, then data drift detection can alert your team to the change. Anomaly detection will alert you when significant outliers appear.

Failover and Fallback What action should you take when a machine learning-based application starts to misbehave in production? You will need time to debug the issue, and that could involve taking the time to contact the data scientists and getting their input. In the meantime, you need to know that your machine learning endpoint is returning something reasonable. Having a fallback model or just a value that you know could suffice, or you can even trigger the fallback automatically for known conditions like timeouts within your code.

Access Controls and Audit Trails Controlling access to your production machine learning applications is critical. Only a limited number of trusted people in your organization should be able to put code into production. Even this group should also have checks on their work, including a deployment administrator and full audit trails of their work. Full audit trails on human and machine actions will also allow you to understand what happened as you are troubleshooting production incidents.

MLOps is much more than just the ability to deploy models into production environments. Successful machine learning in your organization requires trust in machine learning outputs. That trust, at least in part, will come from how you design your security architecture and manage the information security of your machine learning projects.

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Automated Machine Learning is the Future of Data Science – Analytics Insight

As the fuel that powers their progressing digital transformation endeavors, organizations wherever are searching for approaches to determine as much insight as could reasonably be expected from their data. The accompanying increased demand for advanced predictive and prescriptive analytics has, thus, prompted a call for more data scientists capable with the most recent artificial intelligence (AI) and machine learning (ML) tools.

However, such highly-skilled data scientists are costly and hard to find. Truth be told, theyre such a valuable asset, that the phenomenon of the citizen data scientist has of late emerged to help close the skills gap. A corresponding role, as opposed to an immediate substitution, citizen data scientists need explicit advanced data science expertise. However, they are fit for producing models utilizing best in class diagnostic and predictive analytics. Furthermore, this ability is incomplete because of the appearance of accessible new technologies, for example, automated machine learning (AutoML) that currently automate a significant number of the tasks once performed by data scientists.

The objective of autoML is to abbreviate the pattern of trial and error and experimentation. It burns through an enormous number of models and the hyperparameters used to design those models to decide the best model available for the data introduced. This is a dull and tedious activity for any human data scientist, regardless of whether the individual in question is exceptionally talented. AutoML platforms can play out this dreary task all the more rapidly and thoroughly to arrive at a solution faster and effectively.

A definitive estimation of the autoML tools isnt to supplant data scientists however to offload their routine work and streamline their procedure to free them and their teams to concentrate their energy and consideration on different parts of the procedure that require a more significant level of reasoning and creativity. As their needs change, it is significant for data scientists to comprehend the full life cycle so they can move their energy to higher-value tasks and sharpen their abilities to additionally hoist their value to their companies.

At Airbnb, they continually scan for approaches to improve their data science workflow. A decent amount of their data science ventures include machine learning and numerous pieces of this workflow are tedious. At Airbnb, they use machine learning to build customer lifetime value models (LTV) for guests and hosts. These models permit the company to improve its decision making and interactions with the community.

Likewise, they have seen AML tools as generally valuable for regression and classification problems involving tabular datasets, anyway, the condition of this area is rapidly progressing. In outline, it is accepted that in specific cases AML can immensely increase a data scientists productivity, often by an order of magnitude. They have used AML in many ways.

Unbiased presentation of challenger models: AML can rapidly introduce a plethora of challenger models utilizing a similar training set as your incumbent model. This can help the data scientist in picking the best model family. Identifying Target Leakage: In light of the fact that AML builds candidate models amazingly fast in an automated way, we can distinguish data leakage earlier in the modeling lifecycle. Diagnostics: As referenced prior, canonical diagnostics can be automatically created, for example, learning curves, partial dependence plots, feature importances, etc. Tasks like exploratory data analysis, pre-processing of data, hyper-parameter tuning, model selection and putting models into creation can be automated to some degree with an Automated Machine Learning system.

Companies have moved towards enhancing predictive power by coupling huge data with complex automated machine learning. AutoML, which uses machine learning to create better AI, is publicized as affording opportunities to democratise machine learning by permitting firms with constrained data science expertise to create analytical pipelines equipped for taking care of refined business issues.

Including a lot of algorithms that automate that writing of other ML algorithms, AutoML automates the end-to-end process of applying ML to real-world problems. By method for representation, a standard ML pipeline consists of the following: data pre-processing, feature extraction, feature selection, feature engineering, algorithm selection, and hyper-parameter tuning. In any case, the significant ability and time it takes to execute these strides imply theres a high barrier to entry.

In an article distributed on Forbes, Ryohei Fujimaki, the organizer and CEO of dotData contends that the discussion is lost if the emphasis on AutoML systems is on supplanting or decreasing the role of the data scientist. All things considered, the longest and most challenging part of a typical data science workflow revolves around feature engineering. This involves interfacing data sources against a rundown of wanted features that are assessed against different Machine Learning algorithms.

Success with feature engineering requires an elevated level of domain aptitude to recognize the ideal highlights through a tedious iterative procedure. Automation on this front permits even citizen data scientists to make streamlined use cases by utilizing their domain expertise. More or less, this democratization of the data science process makes the way for new classes of developers, offering organizations a competitive advantage with minimum investments.

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Automated Machine Learning is the Future of Data Science - Analytics Insight

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Artificial Intelligence: From Machine Learning to NLP, these are the best 8 reasonable topics for Research … – Gizmo Posts 24

Artificial Intelligence is the technology that strives to develop such machines that can work, act and think like humans.

Visualize a world where machines can also think with humans and work together. This will build an even spare fascinating universe. While this destiny is however far away as Artificial Intelligence has though brought in a ton of progress in these times.

If you crave to research and jot down a thesis based on Artificial Intelligence then there are some reasonable topics.

Instantly without further bustle, lets see the various topics for Research and Thesis on Artificial Intelligence!

It implicates the aim of Artificial Intelligence to facilitate machines to learn a task itself. This procedure begins with nourishing them with good quality data. Then equipping the machines by creating numerous machine learning models utilizing the data and several algorithms.

It is a part of Artificial Intelligence on which the machine memorizes something in a way that is comparable to how humans memorize. Reinforcement Machine Learning Algorithms understand optimal efforts through trial and omission.

It is used to compile and handle the enormous proportion of data that is compelled by the Artificial Intelligence algorithms. In return, these algorithms renovate the data into valuable actionable outcomes that can be enforced by a loT of devices.

This system furnishes you with some suggestions on what to prefer next among the massive options available online. It can be based on- content-based Recommendation or Collaborative Filtering. Assessing the content of all aspects is done by Content-based Recommendations. Assessing your prior lesson behavior and thus recommending topics based on that, is performed by Collaborative Filtering.

5. Computer Vision

It wields Artificial Intelligence to take out information from pictures. This data can be object discretion in the picture, designation of picture subject to organize numerous pictures together, etc.

It is an arena that contracts with building humanoid machines that can act like humans and enact some activities like human entities. AI enables robots to work intelligently in specific conditions.

7. Deep Learning

It wields artificial neural networks to execute machine learning. These neural networks pertain to a web-like structure like a simplified version of the human brain.

8. Natural Language Processing

It is where machines analyze and interpret language and can converse with you. It is presently incredibly prominent for customer assistance applications.

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Artificial Intelligence: From Machine Learning to NLP, these are the best 8 reasonable topics for Research ... - Gizmo Posts 24

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The impact of machine learning on the legal industry – ITProPortal

The legal profession, the technology industry and the relationship between the two are in a state of transition. Computer processing power has doubled every year for decades, leading to an explosion in corporate data and increasing pressure on lawyers entrusted with reviewing all of this information.

Now, the legal industry is undergoing significant change, with the advent of machine learning technology fundamentally reshaping the way lawyers conduct their day-to-day practice. Indeed, whilst technological gains might once have had lawyers sighing at the ever-increasing stack of documents in the review pile, technology is now helping where it once hindered. For the first time ever, advanced algorithms allow lawyers to review entire document sets at a glance, releasing them from wading through documents and other repetitive tasks. This means legal professionals can conduct their legal review with more insight and speed than ever before, allowing them to return to the higher-value, more enjoyable aspect of their job: providing counsel to their clients.

In this article, we take a look at how this has been made possible.

Practicing law has always been a document and paper-heavy task, but manually reading huge volumes of documentation is no longer feasible, or even sustainable, for advisors. Even conservatively, it is estimated that we create 2.5 quintillion bytes of data every day, propelled by the usage of computers, the growth of the Internet of Things (IoT) and the digitalisation of documents. Many lawyers have had no choice but resort to sampling only 10 per cent of documents, or, alternatively, rely on third-party outsourcing to meet tight deadlines and resource constraints. Whilst this was the most practical response to tackle these pressures, these methods risked jeopardising the quality of legal advice lawyers could give to their clients.

Legal technology was first developed in the early 1970s to take some of the pressure off lawyers. Most commonly, these platforms were grounded on Boolean search technology, requiring months and even years building the complex sets of rules. As well as being expensive and time-intensive, these systems were also unable to cope with the unpredictable, complex and ever-changing nature of the profession, requiring significant time investment and bespoke configuration for every new challenge that arose. Not only did this mean lawyers were investing a lot of valuable time and resources training a machine, but the rigidity of these systems limited the advice they could give to their clients. For instance, trying to configure these systems to recognise bespoke clauses or subtle discrepancies in language was a near impossibility.

Today, machine learning has become advanced enough that it has many practical applications, a key one being legal document review.

Machine learning can be broadly categorised into two types: supervised and unsupervised machine learning. Supervised machine learning occurs when a human interacts with the system in the case of the legal profession, this might be tagging a document, or categorising certain types of documents, for example. The machine then builds its understanding to generate insights to the user based on this human interaction.

Unsupervised machine learning is where the technology forms an understanding of a certain subject without any input from a human. For legal document review, the unsupervised machine learning will cluster similar documents and clauses, along with clear outliers from those standards. Because the machine requires no a priori knowledge of what the user is looking for, the system may indicate anomalies or unknown unknowns- data which no one had set out to identify because they didnt know what to look for. This allows lawyers to uncover critical hidden risks in real time.

It is the interplay between supervised and unsupervised machine learning that makes technology like Luminance so powerful. Whilst the unsupervised part can provide lawyers with an immediate insight into huge document sets, these insights only increase with every further interaction, with the technology becoming increasingly bespoke to the nuances and specialities of a firm.

This goes far beyond more simplistic contract review platforms. Machine learning algorithms, such as those developed by Luminance, are able to identify patterns and anomalies in a matter of minutes and can form an understanding of documents both on a singular level and in their relationship to each another. Gone are the days of implicit bias being built into search criteria, since the machine surfaces all relevant information, it remains the responsibility of the lawyer to draw the all-important conclusions. But crucially, by using machine learning technology, lawyers are able to make decisions fully appraised of what is contained within their document sets; they no longer need to rely on methods such as sampling, where critical risk can lay undetected. Indeed, this technology is designed to complement the lawyers natural patterns of working, for example, providing results to a clause search within the document set rather than simply extracting lists of clauses out of context. This allows lawyers to deliver faster and more informed results to their clients, but crucially, the lawyer is still the one driving the review.

With the right technology, lawyers can cut out the lower-value, repetitive work and focus on complex, higher-value analysis to solve their clients legal and business problems, resulting in time-savings of at least 50 per cent from day one of the technology being deployed. This redefines the scope of what lawyers and firms can achieve, allowing them to take on cases which would have been too time-consuming or too expensive for the client if they were conducted manually.

Machine learning is offering lawyers more insight, control and speed in their day-to-day legal work than ever before, surfacing key patterns and outliers in huge volumes of data which would normally be impossible for a single lawyer to review. Whether it be for a due diligence review, a regulatory compliance review, a contract negotiation or an eDiscovery exercise, machine learning can relieve lawyers from the burdens of time-consuming, lower value tasks and instead frees them to spend more time solving the problems they have been extensively trained to do.

In the years to come, we predict a real shift in these processes, with the latest machine learning technology advancing and growing exponentially, and lawyers spending more time providing valuable advice and building client relationships. Machine learning is bringing lawyers back to the purpose of their jobs, the reason they came into the profession and the reason their clients value their advice.

James Loxam, CTO, Luminance

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The impact of machine learning on the legal industry - ITProPortal

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