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Do you know how Google Maps predicts traffic? Are you amused by how Amazon Prime or Netflix subscribes to you just the movie you would watch? We all know it must be some approach of Artificial Intelligence. Machine Learning involves algorithms and statistical models to perform tasks. This same approach is used to find faces in Facebook and detect cancer too. A Machine Learning course can educate in the development and application of such models.

Artificial Intelligence mimics human intelligence. Machine Learning is one of the significant branches of it. There is an ongoing and increasing need for its development.

Tasks as simple as Spam detection in Gmail illustrates its significance in our day-to-day lives. That is why the roles of Data scientists are in demand to yield more productivity at present. An aspiring data scientist can learn to develop algorithms and apply such by availing Machine Learning certification.

Machine learning as a subset of Artificial Intelligence, is applied for varied purposes. There is a misconception that applying Machine Learning algorithms would need a prior mathematical knowledge. But, a Machine Learning Online course would suggest otherwise. On contrary to the popular approach of studying, here top-to-bottom approach is involved. An aspiring data scientist, a business person or anyone can learn how to apply statistical models for various purposes. Here, is a list of some well-known applications of Machine Learning.

Microsofts research lab uses Machine Learning to study cancer. This helps in Individualized oncological treatment and detailed progress reports generation. The data engineers apply pattern recognition, Natural Language Processing and Computer vision algorithms to work through large data. This aids oncologists to conduct precise and breakthrough tests.

Likewise, machine learning is applied in biomedical engineering. This has led to automation of diagnostic tools. Such tools are used in detecting neurological and psychiatric disorders of many sorts.

We all have had a conversation with Siri or Alexa. They use speech recognition to input our requests. Machine Learning is applied here to auto generate responses based on previous data. Hello Barbie is the Siri version for the kids to play with. It uses advanced analytics, machine learning and Natural language processing to respond. This is the first AI enabled toy which could lead to more such inventions.

Google uses Machine Learning statistical models to acquire inputs. The statistical models collect details such as distance from the start point to the endpoint, duration and bus schedules. Such historical data is rescheduled and reused. Machine Learning algorithms are developed with the objective of data prediction. They recognise the pattern between such inputs and predict approximate time delays.

Another well-known application of Google, Google translate involves Machine Learning. Deep learning aids in learning language rules through recorded conversations. Neural networks such as Long-short term memory networks aids in long-term information updates and learning. Recurrent Neural networks identify the sequences of learning. Even bi-lingual processing is made feasible nowadays.

Facebook uses image recognition and computer vision to detect images. Such images are fed as inputs. The statistical models developed using Machine Learning maps any information associated with these images. Facebook generates automated captions for images. These captions are meant to provide directions for visually impaired people. This innovation of Facebook has nudged Data engineers to come up with other such valuable real-time applications.

The aim here is to increase the possibility of the customer, watching a movie recommendation. It is achieved by studying the previous thumbnails. An algorithm is developed to study these thumbnails and derive recommendation results. Every image of available movies has separate thumbnails. A recommendation is generated by pattern recognition among the numerical data. The thumbnails are assigned individual numerical values.

Tesla uses computer vision, data prediction, and path planning for this purpose. The machine learning practices applied makes the innovation stand-out. The deep neural networks work with trained data and generate instructions. Many technological advancements such as changing lanes are instructed based on imitation learning.

Gmail, Yahoo mail and Outlook engage machine learning techniques such as neural networks. These networks detect patterns in historical data. They train on received data about spamming messages and phishing messages. It is noted that these spam filters provide 99.9 percent accuracy.

As people grow more health conscious, the development of fitness monitoring applications are on the rise. Being on top of the market, Fitbit ensures its productivity by the employment of machine learning methods. The trained machine learning models predicts user activities. This is achieved through data pre-processing, data processing and data partitioning. There is a need to improve the application in terms of additional purposes.

The above mentioned applications are like the tip of an iceberg. Machine learning being a subset of Artificial Intelligence finds its necessity in many other streams of daily activities.

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Machine Learning: Real-life applications and it's significance in Data Science - Techstory

At HIMSS20 next month, two machine learning experts will show how machine learning algorithms are evolving to handle complex physiological data and drive more detailed clinical insights.

During surgery and other critical care procedures, continuous monitoring of blood pressure to detect and avoid the onset of arterial hypotension is crucial. New machine learning technology developed by Edwards Lifesciences has proven to be an effective means of doing this.

In the prodromal stage of hemodynamic instability, which is characterized by subtle, complex changes in different physiologic variables unique dynamic arterial waveform "signatures" are formed, which require machine learning and complex feature extraction techniques to be utilized.

Feras Hatib, director of research and development for algorithms and signal processing at Edwards Lifesciences, explained his team developed a technology that could predict, in real-time and continuously, upcoming hypertension in acute-care patients, using an arterial pressure waveforms.

We used an arterial pressure signal to create hemodynamic features from that waveform, and we try to assess the state of the patient by analyzing those signals, said Hatib, who is scheduled to speak about his work at HIMSS20.

His teams success offers real-world evidence as to how advanced analytics can be used to inform clinical practice by training and validating machine learning algorithms using complex physiological data.

Machine learning approaches were applied to arterial waveforms to develop an algorithm that observes subtle signs to predict hypotension episodes.

In addition, real-world evidence and advanced data analytics were leveraged to quantify the association between hypotension exposure duration for various thresholds and critically ill sepsis patient morbidity and mortality outcomes.

"This technology has been in Europe for at least three years, and it has been used on thousands of patients, and has been available in the US for about a year now," he noted.

Hatib noted similar machine learning models could provide physicians and specialists with information that will help prevent re-admissions or other treatment options, or help prevent things like delirium current areas of active development.

"In addition to blood pressure, machine learning could find a great use in the ICU, in predicting sepsis, which is critical for patient survival," he noted. "Being able to process that data in the ICU or in the emergency department, that would be a critical area to use these machine learning analytics models."

Hatib pointed out the way in which data is annotated in his case, defining what is hypertension and what is not is essential in building the machine learning model.

"The way you label the data, and what data you include in the training is critical," he said. "Even if you have thousands of patients and include the wrong data, that isnt going to help its a little bit of an art to finding the right data to put into the model."

On the clinical side, its important to tell the clinician what the issue is in this case what is causing hypertension.

"You need to provide to them the reasons that could be causing the hypertension this is why we complimented the technology with a secondary screen telling the clinician what is physiologically is causing hypertension," he explained. "Helping them decide what do to about it was a critical factor."

Hatib said in the future machine learning will be everywhere, because scientists and universities across the globe are hard at work developing machine learning models to predict clinical conditions.

"The next big step I see is going toward using this ML techniques where the machine takes care of the patient and the clinician is only an observer," he said.

Feras Hatib, along with Sibyl Munson of Boston Strategic Partners, will share some machine learning best practices during his HIMSS20 in a session, "Building a Machine Learning Model to Drive Clinical Insights." It's scheduled for Wednesday, March 11, from 8:30-9:30 a.m. in room W304A.

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Machine learning and clinical insights: building the best model - Healthcare IT News

Pluto7 is a services and solutions company focused on accelerating business transformation. As a Google Cloud Premier Partner, we service the retail, manufacturing, healthcare, and hi-tech industries.

Pluto7 just achieved the Google Cloud Machine Learning Specialization for combining business consultancy and unique machine learning solutions built on Google Cloud.

With Pluto7 comes unique capabilities for machine learning, artificial intelligence, and analytics. Brought to you by a company that contains some of the finest minds in data science, able to draw on its surroundings in the very heart of Silicon Valley, California.

Businesses are looking for practical solutions to real-world challenges. And by that, we do not just mean providing the tech and leaving you to stitch it all together. Instead, Pluto7s approach is to apply innovation to your desired outcome, alongside the experience needed to make it all happen. This is where their range of consultancy services comes into play. These are designed to create an interconnected tech stack and to champion data empowerment through ML/AI.

Pluto7s services and solutions allow businesses to speed up and scale-out sophisticated machine learning models. They have successfully guided many businesses through the digital transformation process by leveraging the power of artificial intelligence, analytics, and IoT solutions.

What does this mean for a partner to be specialized?

When you see a Google Cloud partner with a Specialization, it indicates proficiency and experience with Google Cloud. Pluto7 is recognized by Google Cloud as a machine learning specialist with deep technical capabilities. The organizations that receive this distinction, demonstrates their ability to lead a customer through the entire AI journey. Pluto7 designs, builds, migrates, tests, and operates industry-specific solutions for their customers.

Pluto7 has a plethora of previous experience in deploying accelerated solutions and custom applications in machine learning and AI. The many proven success stories from industry leaders like ABinBev, DxTerity, L-Nutra, CDD, USC, UNM are publically available on their website. These customers have leveraged Pluto7 and Google Cloud technology to see tangible and transformative results.

On top of all this, Pluto7 has a business plan that aligns with the Specialization. Because of their design, build, and implementation methodologies they are able to successfully drive innovation, accelerate business transformation, and boost human creativity.

ML Services and Solutions

Pluto7 has created Industry-specific use cases for marketing, sales, and supply chains and integrated these to deliver a game-changing customer experience. These capabilities are brought to life through their partnership with Google Cloud, one of the most innovative platforms for AI and ML out there. The following solution suites are created to solve some of the most difficult problems through a combination of innovative technology and deep industry expertise.

Demand ML - Increase efficiency and lower costs

Pluto7 helps supply chain leaders manage unpredictable fluctuations. These solutions allow businesses to achieve demand forecast accuracy of more than 90%, manage complex and unpredictable fluctuations while delivering the right product at the right time -- all using AI to predict and recommend based on real-time data at scale.

Preventive Maintenance - Improve quality, production and reduce associated costs

Pluto7 improves the production efficiency of production plants from 45-80% to reduce downtime and maintain quality. They leverage machine learning and predictive analytics to determine the remaining value of assets and accurately determine when a manufacturing plant, machine, component or part is likely to fail, and thus needs to be replaced.

Marketing ML - Increase marketing ROI

Pluto7s marketing solutions improve click-through rates and predict traffic rates accurately. Pluto7 can help you analyze marketing data in real-time to transform prospect and customer engagement with hyper-personalization. Businesses are able to leverage machine learning for better customer segmentation, campaign targeting, and content optimization.

Contact Pluto7

If you would like to begin your AI journey, Pluto7 recommends starting with a discovery workshop. This workshop is co-driven by Pluto7 and Google Cloud to understand business pain points and set up a strategy to begin solving. Visit the website at http://www.pluto7.com and contact us to get started today!

View source version on businesswire.com: https://www.businesswire.com/news/home/20200219005054/en/

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Sierra ShepardGlobal Marketing Teammarketing@pluto7.com

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Pluto7, a Google Cloud Premier Partner, Achieved the Machine Learning Specialization and is Recognized by Google Cloud as a Machine Learning...

TAMPA, Fla. & SEATTLE--(BUSINESS WIRE)--Syniverse, the worlds most connected company, and RealNetworks, a leader in digital media software and services, today announced they have incorporated sophisticated machine learning (ML) features into their integrated offering that gives carriers visibility and control over mobile messaging traffic. By integrating RealNetworks Kontxt application-to-person (A2P) message categorization capabilities into Syniverse Messaging Clarity, mobile network operators (MNOs), internet service providers (ISPs), and messaging aggregators can identify and block spam, phishing, and malicious messages by prioritizing legitimate A2P traffic, better monetizing their service.

Syniverse Messaging Clarity, the first end-to-end messaging visibility solution, utilizes the best-in-class grey route firewall, and clearing and settlement tools to maximize messaging revenue streams, better control spam traffic, and closely partner with enterprises. The solution analyzes the delivery of messages before categorizing them into specific groupings, including messages being sent from one person to another person (P2P), A2P messages, or outright spam. Through its existing clearing and settlement capabilities, Messaging Clarity can transform upcoming technologies like Rich Communication Services (RCS) and chatbots into revenue-generating products and services without the clutter and cost of spam or fraud.

The foundational Kontxt technology adds natural language processing and deep learning techniques to Messaging Clarity to continually update and improve its understanding of messages and clarification. This new feature adds to Messaging Claritys ability to identify, categorize, and ascribe a monetary value to the immense volume and complexity of messages that are delivered through text messaging, chatbots, and other channels.

The Syniverse and RealNetworks Kontxt message classification provides companies the ability to ensure that urgent messages, like one-time passwords, are sent at a premium rate compared with lower-priority notifications, such as promotional offers. The Syniverse Messaging Clarity solution also helps eliminate instances of extreme message spam phishing (smishing). This type of attack recently occurred with a global shipping company when spam texts were sent to consumers with the request to click a link to receive an update on a package delivery for a phantom order.

CLICK TO TWEET: Block #spam and categorize & prioritize #textmessages with @Syniverse & @RealNetworks #Kontxt. #MNO #ISPs #Messaging #MachineLearning #AI http://bit.ly/2HalZkv

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Syniverse offers companies the capability to use machine learning technologies to gain insight into what traffic is flowing through their networks, while simultaneously ensuring consumer privacy and keeping the actual contents of the messages hidden. The Syniverse Messaging Clarity solution can generate statistics examining the type of traffic sent and whether it deviates from the senders traffic pattern. From there, the technology analyzes if the message is a valid one or spam and blocks the spam.

The self-learning Kontxt algorithms within the Syniverse Messaging Clarity solution allow its threat-assessment techniques to evolve with changes in message traffic. Our analytics also verify that sent messages conform to network standards pertaining to spam and fraud. By deploying Messaging Clarity, MNOs and ISPs can help ensure their compliance with local regulations across the world, including the U.S. Telephone Consumer Protection Act, while also avoiding potential costs associated with violations. And, ultimately, the consumer -- who is the recipient of more appropriate text messages and less spam -- wins as well, as our Kontxt technology within the Messaging Clarity solution works to enhance customer trust and improve the overall customer experience.

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About Syniverse

As the worlds most connected company, Syniverse helps mobile operators and businesses manage and secure their mobile and network communications, driving better engagements and business outcomes. For more than 30 years, Syniverse has been the trusted spine of mobile communications by delivering the industry-leading innovations in software and services that now connect more than 7 billion devices globally and process over $35 billion in mobile transactions each year. Syniverse is headquartered in Tampa, Florida, with global offices in Asia Pacific, Africa, Europe, Latin America and the Middle East.

About RealNetworks

Building on a legacy of digital media expertise and innovation, RealNetworks has created a new generation of products that employ best-in-class artificial intelligence and machine learning to enhance and secure our daily lives. Kontxt (www.kontxt.com) is the foremost platform for categorizing A2P messages to help mobile carriers build customer loyalty and drive new revenue through text message classification and antispam. SAFR (www.safr.com) is the worlds premier facial recognition platform for live video. Leading in real world performance and accuracy as tested by NIST, SAFR enables new applications for security, convenience, and analytics. For information about our other products, visit http://www.realnetworks.com.

RealNetworks, Kontxt, SAFR and the companys respective logos are trademarks, registered trademarks, or service marks of RealNetworks, Inc. Other products and company names mentioned are the trademarks of their respective owners.

Results shown from NIST do not constitute an endorsement of any particular system, product, service, or company by NIST: https://www.nist.gov/programs-projects/face-recognition-vendor-test-frvt-ongoing.

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Syniverse and RealNetworks Collaboration Brings Kontxt-Based Machine Learning Analytics to Block Spam and Phishing Text Messages - Business Wire

A few weeks ago I wrote a piece here about Moogsoft which has been making waves in the service assurance space by applying artificial intelligence and machine learning to the arcane task of keeping on keeping critical IT up and running and lessening the business impact of service interruptions. Its a hot area for startups and Ive since gotten article pitches from several other AIops firms at varying levels of development.

The most intriguing of these is a company called Grok which was formed by a partnership between Numenta, a pioneering AI research firm co-founded by Jeff Hawkins and Donna Dubinsky, who are famous for having started two classic mobile computing companies, Palm and Handspring, and Avik Partners. Avik is a company formed by brothers Casey and Josh Kindiger, two veteran entrepreneurs who have successfully started and grown multiple technology companies in service assurance and automation over the past two decadesmost recently Resolve Systems.

Josh Kindiger told me in a telephone interview how the partnership came about:

Numenta is primarily a research entity started by Jeff and Donna about 15 years ago to support Jeffs ideas about the intersection of neuroscience and data science. About five years ago, they developed an algorithm called HTM and a product called Grok for AWS which monitors servers on a network for anomalies. They werent interested in developing a company around it but we came along and saw a way to link our deep domain experience in the service management and automation areas with their technology. So, we licensed the name and the technology and built part of our Grok AIOps platform around it.

Jeff Hawkins has spent most of his post-Palm and Handspring years trying to figure out how the human brain works and then reverse engineering that knowledge into structures that machines can replicate. His model or theory, called hierarchical temporal memory (HTM), was originally described in his 2004 book On Intelligence written with Sandra Blakeslee. HTM is based on neuroscience and the physiology and interaction of pyramidal neurons in the neocortex of the mammalian (in particular, human) brain. For a little light reading, I recommend a peer-reviewed paper called A Framework for Intelligence and Cortical Function Based on Grid Cells in the Neocortex.

Grok AIOps also uses traditional machine learning, alongside HTM. Said Kindiger:

When I came in, the focus was purely on anomaly detection and I immediately engaged with a lot of my old customers--large fortune 500 companies, very large service providers and quickly found out that while anomaly detection was extremely important, that first signal wasn't going to be enough. So, we transformed Grok into a platform. And essentially what we do is we apply the correct algorithm, whether it's HTM or something else, to the proper stream events, logs and performance metrics. Grok can enable predictive, self-healing operations within minutes.

The Grok AIOps platform uses multiple layers of intelligence to identify issues and support their resolution:

Anomaly detection

The HTM algorithm has proven exceptionally good at detecting and predicting anomalies and reducing noise, often up to 90%, by providing the critical context needed to identify incidents before they happen. It can detect anomalies in signals beyond low and high thresholds, such as signal frequency changes that reflect changes in the behavior of the underlying systems. Said Kindiger:

We believe HTM is the leading anomaly detection engine in the market. In fact, it has consistently been the best performing anomaly detection algorithm in the industry resulting in less noise, less false positives and more accurate detection. It is not only best at detecting an anomaly with the smallest amount of noise but it also scales, which is the biggest challenge.

Anomaly clustering

To help reduce noise, Grok clusters anomalies that belong together through the same event or cause.

Event and log clustering

Grok ingests all the events and logs from the integrated monitors and then applies to it to event and log clustering algorithms, including pattern recognition and dynamic time warping which also reduce noise.

IT operations have become almost impossible for humans alone to manage. Many companies struggle to meet the high demand due to increased cloud complexity. Distributed apps make it difficult to track where problems occur during an IT incident. Every minute of downtime directly impacts the bottom line.

In this environment, the relatively new solution to reduce this burden of IT management, dubbed AIOps, looks like a much needed lifeline to stay afloat. AIOps translates to "Algorithmic IT Operations" and its premise is that algorithms, not humans or traditional statistics, will help to make smarter IT decisions and help ensure application efficiency. AIOps platforms reduce the need for human intervention by using ML to set alerts and automation to resolve issues. Over time, AIOps platforms can learn patterns of behavior within distributed cloud systems and predict disasters before they happen.

Grok detects latent issues with cloud apps and services and triggers automations to troubleshoot these problems before requiring further human intervention. Its technology is solid, its owners have lots of experience in the service assurance and automation spaces, and who can resist the story of the first commercial use of an algorithm modeled on the human brain.

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Grok combines Machine Learning and the Human Brain to build smarter AIOps - Diginomica