Big data is an area which has seen rapid growth across a variety of industries in recent years – not least among technology and communications companies.

Figures show that the number of new big data roles being advertised for these companies has increased in recent months with firms across the industry looking to expand their capabilities.

The number of newly advertised roles stood at 47,134 in Q1 2021. That’s up from 34,166 in Q4 2020 and up from 24,235 in Q3 2020.

The figures are compiled by GlobalData, who track the number of new job postings from key companies in various sectors over time. Using textual analysis, these job advertisements are then classified thematically.

GlobalData’s thematic approach to sector activity seeks to group key company information by topic to see which companies are best placed to weather the disruptions coming to their industries.

These key themes, which include big data, are chosen to cover “any issue that keeps a CEO awake at night”.

By tracking them across job advertisements it allows us to see which companies are leading the way on specific issues and which are dragging their heels – and importantly where the market is expanding and contracting.

Where are companies hiring for big data careers in technology?

Looking across key technology and communications companies tracked by GlobalData, the US is currently seeing the largest number of big data job advertisements. Last quarter the country saw 19,197 advertisements – up from 14,891 in Q4 2020 and 11,222 in Q3 2020.

On a city level, Bengaluru (India) had the most newly advertised big data roles in Q1 2021 with 2,074, followed by Hyderabad, (India) with 1,710, and Washington, (US) with 1,444.

Where is seeing the most growth for big data roles in technology?

The biggest growth area has been in India – which saw 4,624 job adverts for big data in Q4 2020, increasing to 10,972 in Q1 2021.

In terms of cities, firms are increasing big data hires in Chihuahua – with 41 roles in the latest quarter – up from zero in the previous one.

The post Where are technology and communications companies focusing their big data hiring efforts? appeared first on Artificial Intelligence.

How AI fights against financial criminals and money launderers?

As criminal methodologies are growing more advanced, the fight against money laundering is becoming a huge challenge for all the financial institutions around the world. Therefore, it becomes necessary to put in AML (Anti-Money Laundering) measures. As AML requires to deal with a huge amount of customer data, they are turning to AI and Machine Learning, to help them identify and detect money laundering activities.

AI performs AML tasks faster than a human employee and also, through machine learning it possesses the capability to modify new threats and detect new money laundering methods. It ensures that financial institutions are able to adjust quickly to different regulatory environments.

When transaction data of a customer is incorporated into an AML program, AI and machine learning models analyze the behavior to make predictions and perceptions about that customer in the future.

How are AI and Machine Learning advantageous in fighting financial criminals and money launderers?

Customer Perceptions

AI systems enable the CDD (Customer Due Diligence) and KYC (Know Your Customer) systems, to take place at a faster rate and with greater deepness and reach. The AI-based CDD and KYC processes enable the financial institution to

Efficiently identify and collect data from a greater range of external sources which include watch lists, sanction lists, and create a factual profile of the customer.

Recognize valuable owners of customer entities by using external data faster and more efficiently.

Accumulate and reconcile customer data across internal systems to remove replication and errors and intensify the density of AML measures among customers.

Automatically enhance dubious activity reports with appropriate data from customer risk profiles or data from external sources.

Unstructured Data

There are other important steps beyond creating customer risk profiles. As a part of monitoring transactions, screening PEP, screening sanctions, and monitoring media, the AML process requires identifying and analyze the unstructured data. Every financial institution must make an effort to use the unstructured data to recognize their professional, social and political lives by inspecting a range of external sources which includes public archives, media, social networks, etc. in such circumstances, AI helps the institution to recognize those unstructured data. Once the data is collected and analyzed, AI helps the institution prioritize and categorize information to assist risk management.

Reporting Dubious Activity

AI can assist the reporting of doubtful activity by producing reports and also, by automatically filling them with accurate information. After their submission of reports to the authority, SARs goes through a process of internal reporting. AI technology can make the SAR process easy as algorithms can generate automated reports with accurate data and transmute that data into an accessible, standardized language in order to eliminate bureaucratic friction. Because of standardized language and terminology, AI increases the speed and efficiency of an institution’s AML reporting.

Noise Minimization

The AML system is complex and is a time-consuming procedure therefore it is an advantage to incorporate AI within an AML system which helps in adding speed and efficiency. But one of the major hindrances in the process is the level of noise or false positives which is the result of incomplete or inadequate data or over-sensitivity of AML steps. In such cases, AI systems play an important role by generating a significant transformative effect to the level of noise generated during the AML process. AI assists the institution to produce higher insight into customer’s transaction patterns and enables them to remove wrong and invalid alerts which makes the process costly for the institutions and inconvenient for customers. By minimizing noise, AI and machine learning tools enable AML employees to better prioritize and direct the most required money laundering alerts. By doing so AI more effectively contributes to the fight against financial crime.

Limitations of AI

In order to keep pace with the increasing risk of financial criminals and money launderers and the need to react faster to those new threats, often new AI and machine learning models are prematurely dashed into the market without proper training. This creates a huge skepticism around AI and Machine Learning technologies. Therefore, banks must remember that AI experimentation comes with diminishing returns. They should focus on performing strategic, production-ready AI micro-projects in parallel with human teams to deliver actionable insights and value.

The post AI: TECHNOLOGY TO FIGHT FINANCIAL CRIMINALS AND MONEY LAUNDERERS appeared first on Artificial Intelligence.

How does cloud computing lead to improved healthcare outcomes?

The Healthcare industry is one of the fastest-growing sectors with a CAGR of 5% from 2019 to 2023. This is due to issues like the spread of chronic diseases, the aging population, and more. In the healthcare industry, a huge amount of data is created on a daily basis. Therefore, there is a need for the creation, storage, usage, and sharing of healthcare data. This caters to the need for cloud computing for quick, cost-effective and safe, and secure solutions.

Cloud computing to be precise, is the delivery of computing services (such as servers, networking, databases, analytics, intelligence, and storage) over the cloud (internet) to provide flexible resources, economies of scale, and faster innovation. Cloud computing helps in reducing operating costs, running infrastructure more effectively, etc. Thus, cloud computing enables the healthcare industry to break free from limitations while offering better patient outcomes.

Shifting to the cloud computing service is advantageous for both healthcare providers as well as for patients. The healthcare cloud computing trend is expected to grow to 64.7 billion US dollars by 2025.

Cloud Computing in Healthcare Sectors

Cloud computing assists healthcare providers in reducing operational expenses while providing improved, personalized care. Also, it helps to drive efficient workflows thus leading to better service. Parallel to this, cloud computing helps the patients to receive quick responses from the healthcare industry and also helps them to keep track of their health better through healthcare data offered by cloud solutions.

Healthcare providers and hospitals across the globe are increasing the application of cloud computing to solve problems like care coordination, data security, and population health.

Advanced cloud computing allows medical research innovation by helping researchers access important data sets under solid control. Cloud has also proved a powerful tool in hospital settings.

Advanced compute, storage capability, and database, combined with artificial intelligence and machine learning tools help the healthcare industry in building solutions and providing services to the patients. Innovative cloud computing technology will serve to equal access to care, enable the healthcare provider to make the best decision, and also access to significant medical research.

Enhanced database capabilities and advanced computing power are making preventative population health analysis a reality.

How does Cloud Computing lead to improved healthcare outcomes?

Cost Reduction

The basic theory of cloud computing is the availability of computer services like data storage and computing power. Therefore, this frees the hospitals and healthcare providers from the purchase of hardware and servers for storage. There are no charges kindred with cloud storage of data. The healthcare industry can only pay for the resources they use and thus results in huge cost savings. Cloud computing also offers an efficient environment for scaling which is highly desirable by healthcare providers for improved healthcare service.

Easy Interoperability

Cloud computing helps in establishing data combinations (irrespective of the point of origin or storage) throughout the healthcare system. This process is called Interoperability. As a result of interoperability data of the patients become easily available for distribution. This helps in gaining visions to advance healthcare planning and delivery. Thus, cloud computing enables healthcare providers to get easy access to the patient data collected from various sources and provide prescriptions and treatment protocols on time.

Approach to high powered analytics

The Healthcare industry contains data that are both structured as well as unstructured. Cloud helps in collecting and collating important data of the patients from different sources. With the modern computing power of the cloud, the processing of large datasets becomes more viable. The application of Big Data analytics and Artificial Intelligence on patient data also paves the way for developing more advanced care plans for patients.

Patient’s access to data

Cloud computing allows patients to get control over their own health. Cloud computing makes it accessible for the patients to get hold of their data from the cloud and this leads to patient participation in making decisions of their own health.

Telemedicine Capabilities

Cloud storage of data offers remote accessibility of data. The integration of cloud computing with the healthcare industry carries the ability to improve a number of healthcare-related functions such as telemedicine, virtual medication, and post-hospitalization care plans. Telemedicine apps add the element of convenience to healthcare delivery while upgrading the patient experience. Cloud-based telehealth systems and applications allow easy sharing of healthcare data, improve accessibility and provide healthcare coverage to the patients during the preventative, treatment as well as recovery phase.

The post CLOUD COMPUTING: TECHNOLOGY BEHIND IMPROVED HEALTHCARE SECTORS appeared first on Artificial Intelligence.

When thinking of technology, one cannot go without talking about deep learning. Needless to say, deep learning has become one of the most critical aspects of technology. Gone are the days when organizations alone used to show interest in technologies like AI, deep learning, machine learning, etc. Today, even individuals are inclined towards the very aspect of technology, deep learning in particular. One of the many reasons why deep learning draws all the attention is because of its ability to enable improved data-driven decisions and also improve the accuracy of the predictions made.

In a nutshell, companies are in a position to reap out various financial and operational benefits by virtue of deep learning. With many deep learning innovations proliferating with time, it makes every possible sense to have a clear picture as to how does the future of deep learning looks like. In line with what we have seen over the past few years, this is what we could expect in the coming days as far as deep learning is concerned –

• Despite the fact that deep learning is a little on the slower side when compared to traditional AI and other machine learning algorithms, what one can stay assured of is the fact that it is way more powerful as well as straightforward. It is because of this that fields such as medicine, supply chain, robotics, manufacturing, etc. would see immense usage of deep learning in the days that lie ahead.

• A few years from now, it is very much possible that deep learning development tools, libraries, and languages could become standard components of every software development tool kit. These tool kits with modern capabilities will pave the way for easy design, configuration, and training of new models. With these capabilities, style transformation, auto-tagging, music composition, etc. would be a lot easier to accomplish.

• The need for faster coding is at an all-time high. The future is all set to see the deep learning developers adopting integrated, open, cloud-based development environments that provide access to a wide range of off-the-shelf and pluggable algorithm libraries.

• The prediction that neural architecture search would play a pivotal role in building data sets for the deep learning models still stands strong.

• Global marketers have a positive mindset by virtue of Google’s acquisition of DeepMind Technologies.

• It is highly likely that the deep learning networks would demystify computer memory.

• Yet another point that is worth making a note of is the fact that the automation of deep learning tools would mean that there’s an inherent risk that could develop into something so complex that the average developers will find themselves totally ignorant.

• Deep learning should be able to demonstrate learning from limited training materials and transfer learning between contexts, continuous learning, and adaptive capabilities. Wondering why. Well, just to remain useful.

What everything boils down to is the fact that as a result of the growing popularity of deep learning and with the advancement in technology, by the end of this decade, the deep learning industry will simplify its offerings considerably so that they’re comprehensible and useful to the average developer.

The post THE FUTURE OF DEEP LEARNING appeared first on Artificial Intelligence.

Let’s Check on The Top 10 Highest Paying Jobs in Technology for 2021.

With the evolution of modern technology and wholesome digital transformation, there is enormous availability of the highest-paid jobs in technology. In the past career options were limited, there were only a few jobs in specific industries like medicine or IT. But now there are ample opportunities where one person can get certified even when they are still working.

Want to give your career a good start and to make sure about your skillset?

Here is the list of the top 10 highest-paid tech jobs for you.

1. Data Scientist

For any organization today, data is the most valuable asset. Therefore, the key responsibility of a data scientist stands to analyse and identify all data that are collected and then interpret the complex variables and forms of those data. For becoming a data scientist, one needs to have enough knowledge about machine learning, data visualization, deep learning, mathematics, computer science.

Job role for data scientists-

• Constructing data models
• Coding languages like Python and other analytical tools
• Understanding and working with machine learning algorithms
• Detecting business problems and providing solutions
• Data scientists can earn as much as $150,000 per annum

• TOP 10 DEEP LEARNING ALGORITHMS ONE SHOULD KNOW IN 2021
• TOP 10 BIG DATA STATISTICS YOU MUST KNOW IN 2021
• LEARN TOP PYTHON COURSES ONLINE TO UPGRADE YOUR SKILLS

2. Big Data Architect

Users generate over 2.5 quintillion bytes of data daily, but this data in its raw form is impractical and useless. Therefore, over 98% of organizations are investing in Big Data to identify and analyze these data and interpret them. Here stands the need for Big Data architects. Job role for Big Data architect- planning, designing, and managing large-scale deployments of Big Data applications from beginning to end. Skills required for this role are programming skills, data visualization skills, communication skills.

The average salary for a Big Data architect is $140,000 per annum.

3. IoT (Internet of Things) Solutions Architect

One of the fastest-growing technology today is the Internet of Things (IoT). It caters to swift and flawless data sharing by using AI and machine learning. The IoT solutions architect connects the gap between technical and non-technical roles. An IoT solutions architect is responsible for leading and participating in all activities related to architecture and design. An IoT architect should have skills such as hardware design and architecture, strong programming skills, knowledge of Machine Learning.

IoT Architects can earn an average of $130,000 per annum.

4. Software Architect

The main job of a software architect is optimizing the development process by making choices related to design and creating technical standards of coding, tools, and platforms. They are hired by the organization for testing and developing software for them. The job role for software architects is understanding customer demands and perform hands-on work to develop software prototypes. Mandatory skills to become a software architect are understanding software architecture, programming skills, strong analytical skills, and data modeling.

The demand for software architects is very high, with organizations paying well over $114,000 per annum.

5. Blockchain Engineer

This technology constitutes links of chains of information and data that keep getting added to a particular network. A blockchain engineer with the help of blockchain technology develops and implements architecture and solutions. Thorough understanding of Ripple, R3, Ethereum, Bitcoin, etc; understanding of consensus methodologies and the security protocol stacks, crypto libraries, and functions; strong programming skills are required for a blockchain engineer.

As a blockchain engineer, one can earn an average of $150,000 a year.

6. Artificial Intelligence (AI) Architect

The task of an AI architect is to define, identify, design, and build solutions that cater to growth and productivity for organizations through automation. To become an AI architect, skills such as deep knowledge of mathematics and statistics, Solid programming skills with knowledge of Python, R, and Torch, working knowledge of TensorFlow and similar technologies, understanding of technologies related to AI like Machine Learning, neural networks, and Deep Learning are required.

An average salary of an AI architect is $110,000 per annum.

7. Cloud Architect

Cloud computing is a mainstream technology that organizations and individuals across the world use on a daily basis. This resulted in openings for many new jobs as a cloud architect. The task of a cloud architect is to help an organization to develop cloud architecture, cloud strategy, coordinating its execution and application. To become a cloud architect, one needs to have a deep understanding of cloud application architecture, knowledge of Amazon Web Services (AWS), Microsoft Azure, or Google cloud platform, excellent communication skills.

A cloud architect can earn $107,000 per annum on an average in this field.

8. Full-Stack Developer

The task of a full-stack developer is to control and develop both the front-end and back-end of an organization. A full-stack developer must have knowledge about technologies like MongoDB and Node.js, coding and scripting, basics of database technology, fundamentals of web development and should know how to design and develop an API.

The average income of a full-stack developer is $106,000 per annum.

9. DevOps Engineer

The job of a DevOps engineer is processing tools and methodologies that are required to stabilize the needs through the life cycle of the software development process. A DevOps engineer designs and maintains a deployment structure, applies cloud services to allow mechanization in Python and Ruby. To become a DevOps engineer, a person must know coding and scripting, deployment and network operations, to use tools like Git and Jenkins, Using Linux or Unix system administration.

Their average annual salary ranges from $95,000 to $140,000.

10. Product Manager

A product manager is responsible to help the organization in identifying parameters for a product that is built by the engineering team. A product manager is required to lead the development process for a product from its conception to its launch. Product managers also identify the vision of a product and work thoroughly with other teams like sales, marketing, engineering, and IT. A product manager must have a thorough understanding of the concept of PLM, should have the knowledge to manage product tools PivotalTracker, JIRA, or Asana, must have analytical skills and strong time management skills to meet the deadline.

The post TOP 10 HIGHEST PAYING JOBS IN TECHNOLOGY, 2021 appeared first on Artificial Intelligence.

These Big Data Courses will Help you get High Paying Jobs.

Big data, is a valuable technology for industries all across the globe. Tech giants like eBay, NASA, Amazon, Google, and Facebook use big data to make better business decisions and get a sense of all the available data. If you are a tech enthusiast who wants to get a high-paying career in the tech world, big data is a lucrative field.

• BIG DATA IMPLEMENTATION FOR SUCCESSFUL DAILY SUPPLY MANAGEMENT
• 5 TRENDS THAT WILL DETERMINE THE FUTURE OF BIG DATA TECHNOLOGIES
• BIG DATA ENGINEER VS AI ENGINEER: WHICH CAREER IS BETTER?
• EVERYTHING YOU NEED TO KNOW ABOUT DATA SCIENCE, BIG DATA AND DATA ANALYTICS

If you’re a beginner, here are the best big data courses for you to get started.

1. Big Data Analytics

Duration: 10 weeks

This course will teach you essential skills in today’s digital age to store, process, and analyze data to inform business decisions. This course will cover topics ranging from cloud-based big data analysis, predictive analytics including probabilistic and statistical models, application of large-scale data analysis, and analysis of problem space and data needs. By the end of this course, you will be able to approach large-scale data science problems with creativity and initiative.

2. Big Data and Hadoop

Through this course, you will understand the complex architecture of Hadoop and its components. It covers everything that you need as a Big Data Beginner, such as the Big Data market, different job roles, technology trends, history of Hadoop, HDFS, Hadoop Ecosystem, Hive, and Pig. This course also comes with a lot of hands-on examples that will help you learn Hadoop quickly.

3. Introduction to Big Data

This course is for tech enthusiasts who want to learn data science and are interested in understanding why the Big Data Era has become prominent. Through this course, you will learn about big data landscapes and real-world big data problems, important aspects of big data like volume, velocity, valence, value, and how they impact data collection, monitoring, storage, analysis, and reporting. This beginner needs no prior programming experience.

4. IoT Programming and Big Data

Duration: 5 Weeks

This course will teach you the introductory programming concepts that will help you understand IoT devices using the Python programming language. In addition, you will learn how to use Python to process text log files, such as those generated automatically by IoT sensors and other network-connected systems. No prior programming experience is required to enroll in this course.

5. Data Engineering Foundations

This course will take you through the fundamentals of data engineering. With a range of topics like data wrangling, database schema, and developing ETL pipelines, you will also experience several data engineering tools like Hive, Hadoop, Spark, and Airflow. By the end of this course, you will know the scope of data engineering in a data-driven organization.

6. Big Data in the Age of AI

Big data builds the foundations for many disruptive technologies that are crucial for businesses like AI and machine learning. In this non-technical course, you will learn how big data is shaping our data-driven world. Further, this course also digs into big data’s connection with AI, data science, social media, IoT, and ethical issues behind data.

7. Foundations for Big Data Analysis with SQL

This course will show you the big picture of using SQL for big data, starting with an overview of data, database systems, and the common querying language (SQL). By the end of this course, you will be able to distinguish operational from analytic databases and understand how these are applied in big data, understand how database and table design provides structures for working with data, appreciate how differences in volume and variety of data affect your choice of an appropriate database system, recognize the features and benefits of SQL dialects designed to work with big data systems for storage and analysis, and explore databases and tables in a big data platform.

8. Data Ethics, AI, and Responsible Innovation

Duration: 7 weeks

In a world where we are surrounded by data, it is important to know how much control data has over us and vice versa. In this course, you will understand the ethical issues in the data lifecycle, learn about digital rights, data governance, responsible research, innovation, and apply critical judgment to solve moral problems with clear solutions.

9. Computational Thinking and Big Data

Duration: 10 weeks

Computational thinking is a skill that is critical for several industries to formulate a problem and express solutions for computers to work on. In this course, you will understand and apply advanced core computational thinking concepts to large-scale data sets, use industry-level tools for data preparation and visualization, such as R and Java, apply methods for data preparation to large data sets, understand mathematical and statistical techniques for attracting information from large data sets and illuminating relationships between data sets.

10. Taming Big Data with Apache Spark and Python

This course will teach you the hottest big data technology, Apache Spark. You will learn the concepts of Spark’s DataFrames and Resilient Distributed, develop and run Spark jobs quickly using Python, translate complex analysis problems into iterative or multi-stage Spark scripts, scale up to larger data sets using Amazon’s Elastic MapReduce service, understand how Hadoop YARN distributes Spark across computing clusters, and learn about other Spark technologies, like Spark SQL, Spark Streaming, and GraphX.

The post BEST BIG DATA COURSES FOR BEGINNERS TO ACE THE TECHNOLOGY appeared first on Artificial Intelligence.

Virtually all businesses today rely on digital technology to drive major operations, including upstream and downstream supply, sales and marketing, recruitment and onboarding, and internal communication. This is a partial list, but a telling one – symbolic of the reasoning behind the statement that “every company is now a technology company.”

The extent to which organizations use technology in the 21st century has a profound impact on growth, profitability, and overall success. As leaders and technologists increasingly work within the same domains, here are the five best practices that should be observed to leverage the skills of both and drive businesses forward:

1. Do not lead with technology in mind.

Technology’s starting point for driving ongoing business success is helping define the complex business problems in need of solutions and employing strategies that will most effectively support growth. The technology itself often plays a big part, but it should never be the primary focus.

Instead, leaders should always put stakeholders first. Customers, investors, and employees are the core building blocks of business, and fulfilling their needs (both stated and unstated) is the main driver of long-term growth and success.

2. Do not allow technology to remain siloed.

Technology has historically been viewed as a back-end function, receiving instructions and carrying them out independently. However, the divide between strategy and technology can be viewed as a fundamental misunderstanding of how to operate in the information age: Digital transformation is not only about implementing more and better technology but also about overlaying all traditional business processes with the power of those technologies offer.

This means it’s vital for executives to drive a genuine paradigm shift where data and software are concerned. Allowing technology to function as a disconnected department or entity limits its vast potential, along with that of the organization it is meant to serve.

3. Use data as the fulcrum for strategic advantage.

Seismic shifts in technology (artificial intelligence, machine learning, and the advent of microprocessing, to name a few) led to a tidal wave of data. Companies in many industries now access billions of unique data points on a daily basis. All of this information can provide insight regarding many important questions, including:

• How are customers interacting with products and perceiving brands?
• Which customer touchpoints are most valuable?
• Which moments are most critical in the customer journey?
• How well are products and services performing throughout their lifecycle?
• What shifts in the market are happening or are imminent, and how might they affect operations?

Answers to questions like these are an integral part of an evolving business strategy. The value of information is compounded by the speed at which it can be processed, and leveraging fast data is now not only a possibility for most organizations but a necessity to stay competitive.

4. Make technology the center of innovation.

Companies that lead in innovation often lead in the marketplace as well. Digital platforms and tools not only allow companies to ask smarter questions but also facilitate fast and easy implementation of ad hoc solutions testing.

When technology was hardware-focused, research and development was an expensive, time-consuming investment. Today, largely due to a shift to cloud infrastructure, it is a quick and highly dynamic operation with the ability to set companies apart in their sector. Multiple solutions can be experimented with simultaneously, providing deep answer sets and generating additional data in the process.

5. Prioritize the problems that arise at the juncture of technology and business.

In many cases, technology alone can do little to alter the trajectory of an organization.

However, it exponentially increases the value of traditional business knowledge by giving leaders the ability to ask and answer more complex questions.

“Business as usual” in most industries today is technologically enhanced and massively empowered when compared to just two decades ago. And yet, much of technology’s potential remains latent. Leaders who wish to activate it should start by understanding that the bottleneck is still fundamentally human. Internal barriers need to be broken down, and clear communication lines must be established between historically siloed teams. When this takes place, the true power of technology’s ability to innovate when it comes to customer experience and drive business growth can be unleashed – and the organizations that implement it will lead us into the next era of business.

The post 5 BEST PRACTICES FOR INFLUENCING BUSINESS STRATEGY WITH TECHNOLOGY appeared first on Artificial Intelligence.

With so many advances in technology and analysis tools, it’s getting hard for traders to keep up. One of the highly discussed topics is machine learning. If you want to know where these two fields intersect, let’s first clarify what each of the terms means.

How Does Forex Trading Work?

Foreign exchange, or Forex, is the process of converting one currency into another. The value of every specific currency is determined by market factors such as trade, investment, tourism, and geopolitical risk.

Forex is commonly traded in specific amounts called lots, which are basically the number of currency units that you will purchase or sell. The standard lot size is 100,000 units of currency.

There are three main methods to trade Forex that are commonly used by traders as per their objectives:

• The spot market – This is the principal Forex market, where currency pairings are switched in real time, and exchange rates are set based on supply and demand. This trading constitutes a “direct exchange” of two currencies, has the shortest time frame, includes cash rather than a contract, and the agreed-upon transaction does not include interest. One of the most prevalent forms of Forex trade is spot trading.
• The forward market – In this type, instead of immediately completing a trade, Forextraders can enter into (private) contracts with another trader to lock in an exchange rate with a certain volume of currency at a future date, regardless of what the market rates are then.
• The futures market – Similarly, traders can choose to purchase or sell a fixed sum of a currency at a certain exchange rate at a future date. Unlike the forwards market, this is done on an exchange rather than privately, where the traders are entered into a legally binding contract.

What Is Machine Learning?

Machine learning (ML) is the study of computer algorithms that improve automatically over time via experience and the use of data. It is considered a branch of artificial intelligence. Since new technology has made trading faster and easier, ML is increasingly becoming significant in the Forex trading world.

In order to implement Machine Learning in Forex trading, one must first create algorithms. These algorithms examine data in order to spot trends and forecast future events.

Algorithmic Tools Used in Forex

In Forex trading, a wide array of algorithmic tools based on machine learning are applied, including:

SVM

SVM or a Support Vector Machine is a data categorization machine learning language. Because of its ease of application in data categorization challenges, the language has gained widespread acceptance. SVMs work by splitting data sets using decision boundaries.

SVM is used to anticipate or assess if a market trend is bullish or bearish using this method in Forex trading. This is accomplished by establishing hyper-planes between a trend’s highs and lows. A forward hyper-plane denotes a bullish trend, while a backward hyper-plane denotes a bearish trend (hyper-planes), and then classifies fresh data using the hyper-planes.

Neural Network

Neural Network in Forex is a machine learning method that analyses market data (technical and fundamental indicator values) and tries to anticipate the target variable (close price, trading result, etc.). It is inspired by how human biological neurons operate.

In Forex, there are two primary issues of contention: the Forex regression problem, in which we attempt to forecast future trends, and the Forex classification problem, in which we attempt to forecast whether a trade will be successful or not. The Neural Network addresses these two problems by keying in yesterday’s high and low price with the last seven day’s high and low price to predict tomorrow’s price.

Why Use Machine Learning in Forex?

In the Forex trading world, ML can be used for a variety of purposes:

• The use of ML to monitor pricing in real time has led to greater transparency. ML algorithms can make buying/selling of lots automatic in the Forex market, thereby providing traders an edge with speed and precision.
• ML involves keying in historical data to a system so that it can make future decisions based on it. As a result, ML uses past data, referred to as predictor variables, to forecast present currency values, which are referred to as target variables. In order to do so, the ML algorithm learns to use predictor variables to forecast target variables.

With the help of a supervised ML model, the predicted uptrend or downtrend of Forex rate might help traders to make the right decision on Forex transactions since the decisions made are fact-based, unlike human beings whose decisions are driven by emotions like fear, greed, and hope.

ML also assists in expanding the number of marketplaces that a trader can monitor and respond to. The higher the number of marketplaces available, the more likely a trader will choose the most profitable one. As a result, by implementing ML, traders can optimize their profits and diminish their risks.

Conclusion

The foreign exchange market is the world’s largest financial market, and it isn’t going away anytime soon. ML has been a game-changer in the field of Forex trading with its fast-paced automated trading, which needs no human intervention and provides accurate analysis, forecasting, and timely execution of the trades. And for mitigating the risks, ML plays an important role in shaping the future of Forex trading.

The post MACHINE LEARNING IN FOREX TRADING appeared first on Artificial Intelligence.

“AI” is becoming a construct that has been the subject of increasing attention in technology, media, business, industry, government and civil life during recent years.

Today’s AI is the subject of controversy. You might have heard about narrow/weak, general/strong/human level and super artificial intelligence, or about machine learning, deep learning, reinforced learning, supervised and unsupervised learning, neural networks, Bayesian networks, NLP, and a whole lot of other confusing terms, all dubbed as AI techniques.

Many of the rules and logic-based systems that were previously considered Artificial Intelligence are no longer AI. In contrast, systems that analyze and find patterns in data are dubbed as machine learning, widely promoted as the dominant form of AI.

What is Wrong with Today’s AI, Its Chips and Platforms?

All the confusion comes from an anthropomorphic Artificial Intelligence, AAI, the simulation of the human brain using artificial neural networks, as if they substitute for the biological neural networks in our brains. A neural network is made up of a bunch of neural nodes (functional units) which work together, and can be called upon to execute a model.

Thus, the main purpose in 2021 is to provide a conceptual framework to define Machine Intelligence and Learning. And the first step to create MI is to understand its nature or concept against main research questions (why, what, who, when, where, how).

So, describe AI to people as an AAI or augmented intelligence or advanced statistics, not artificial intelligence or machine intelligence.

Now, re the levels of AAI applications, tools, and platforms?

Lets focus only on “AAI chips”, forming the brain of an AAI System, replacing CPUs and GPUs, and where most progress has to be achieved.

While typically GPUs are better than CPUs when it comes to AI processing, they usually fail, being specialized in computer graphics and image processing, not neural networks.

The AAI industry needs specialised processors to enable efficient processing of AAI applications, modelling and inference. As a result, chip designers are now working to create specialized processing units.

These come under many names, such as NPU, TPU, DPU, SPU etc., but a catchall term can be the AAI processing unit (AAI PU), forming the brain of an AAI System on a chip (SoC).

It is also added with 1. the neural processing unit or the matrix multiplication engine where the core operations of an AAI SoC are carried out; 2. Controller processors, based on RISC-V, ARM, or custom-logic instruction set architectures (ISA) to control and communicate with all the other blocks and the external processor; 3. SRAM; 4. I/O; 5. the interconnect fabric between the processors (AAI PU, controllers) and all the other modules on the SoC.

The AAI PU was created to execute ML algorithms, typically by operating on predictive models such as artificial neural networks. They are usually classified as either training or inference generally performed independently.

AAI PUs are generally required for the following:

• Accelerate the computation of ML tasks by several folds (nearly 10K times) as compared to GPUs
• Consume low power and improve resource utilization for ML tasks as compared to GPUs and CPUs

Unlike CPUs and GPUs, the design of single-action AAI SoC is far from mature.

Specialized AI chips deal with specialized ANNs, and are designed to do two things with them: task-designed training and inference, only for facial recognition, gesture recognition, natural language processing, image searching, spam filtering, etc.

In all, there are {Cloud, Edge, Inference, Training} chips for AAI models of specific tasks. Examples of Cloud + Training chips include NVIDIA’s DGX-2 system, which totals 2 petaFLOPS of processing power, made up of 16 NVIDIA V100 Tensor Core GPUs, or Intel Habana’s Gaudi chip or Facebook photos or Google translate.

Sample chips here include Qualcomm’s Cloud AI 100, which are large chips used for AAI in massive cloud datacentres. Another example is Alibaba’s Huanguang 800, or Graphcore’s Colossus MK2 GC200 IPU.

Now (Cloud + Inference) chips were used to train Facebook’s photos or Google Translate, to process the data you input using the models these companies created. Other examples include AAI chatbots or most AAI-powered services run by large technology companies. Here is also Qualcomm’s Cloud AI 100, which are large chips used for AAI in massive cloud datacentres, Alibaba’s Huanguang 800, or Graphcore’s Colossus MK2 GC200 IPU.

(Edge + Inference) on-device chips examples include Kneron’s own chips, including the KL520 and recently launched KL720 chip, which are lower-power, cost-efficient chips designed for on-device use; Intel Movidius and Google’s Coral TPU.

All of these different types of chips, training or inference, and their different implementations, models, and use cases are expected to develop the AAI of Things (AAIoT) future.

How to Make a True Artificial Intelligence Platform

In order to create a platform neutral software operating with world’s data/information/content which could run/display properly on any type of computer, cell phone, device or technology platform, the following are required:

• Operating Systems.
• Computing/Hardware/Cloud Platforms.
• Database Platforms.
• Storage Platforms.
• Application Platforms.
• Mobile Platforms.
• Web Platforms.
• Content Management Systems.

The AI programming language should act as both the general programming language and computing platform. Its applications could be launched on any operating system and hardware, from mobile-based operating systems, as Linux or Android, to hardware-based platforms, from game consoles to supercomputers or quantum machines.

The post HOW TO CREATE AN ARTIFICIAL INTELLIGENCE GENERAL TECHNOLOGY PLATFORM appeared first on Artificial Intelligence.

Machine intelligence is the combination of AI and ML

Serving dishes, controlling traffic, performing surgeries on humans – think of these and the first impression is that you cannot do without humans here. The situation now seems to have undergone a 360 degree transformation. Gone are the days when every task that you can think of needed human intervention. Now, you find machines taking up the role of waiters, traffic controllers, educators and what not. One of the greatest achievement is in the field of healthcare sector. Machines are assisting doctors and surgeons while performing medical procedures. We have reached a stage wherein some not so difficult procedures are done by machines themselves without the involvement of humans.

This machine intelligence has truly transformed the way we look at things. This kind of intelligence has made it easy to address issues and problems in every field like never before. The reason why machines are intelligent to the extent that they hold the potential to perform tasks just like humans is because of Artificial Intelligence. It is only by virtue of Artificial Intelligence that we get to see human-like machines and computers. This area will see a lot more advancements in the near future, without a doubt. With AI, machines are capable of interacting in an intelligent way. Contrary to popular belief, it is not because of the fact that machines are able to perform a couple of tasks like humans that makes them intelligent. The story goes beyond all of this.

An intelligent machine, system, hardware or any computer is not intelligent because it is able to perform human-like tasks. It is solely because such machines stand the potential to complete tasks in an unreliable environment. Unlike what they are being asked to do, machines are intelligent if they can judge what’s going around by being able to monitor the environment and then acting accordingly. Just imagine how a person would react to different situations. Same is the case with machines. If a person is able to make the right use of intelligence, it is then that he / she is said to be intelligent. If the similar criteria is followed in case of machines and they are able to react just like humans by making the best use of their intelligence, then that is what constitutes an intelligent machine.

Probably the best examples of intelligent machines are Alexa and Siri. Not forgetting to mention here, how popular they have become over a period. Also, their demand continues to rise – thanks to AI. It is impossible to imagine machines being intelligent without Artificial Intelligence in place. It is solely because of AI that the machines can come up with improved decisions for the company. They do this by accessing information in the best manner possible.

What constitutes Machine intelligence?

When talking about machine intelligence, there are two concepts that are critical and form the base of the origin – Artificial Intelligence and machine learning. A combination of these two is the reason why machines are proactive. These two allow the machines to not just collect the data but also process it to arrive at conclusions. Basis these conclusions, the organizations make decisions. To make machines work human-like, naturally, some aspects of humans will have to be incorporated. Skills like problem solving, learning ability, prioritization, etc. go in the making of machine intelligence. Needless to say, programming has to be the pre-requisite. Also, machines are designed keeping in mind the concept of “deductive logic”. Using this, they are well aware of when they have made mistakes. Learning from this, the machines ensure that the same mistake isn’t committed againin the future.

Though not many skills go into making machines intelligent, the way they handle the situations and tackle problems does come out to be surprising.

It is because of this that companies are inclined towards machine intelligence. They include a set of automation techniques and develop a model that’d help them achieve their goals. This form of intelligence has eased a lot of issues and hence will continue to rule for the years to come.

The post MACHINE INTELLIGENCE IS HERE AT THE TECHNOLOGY SECTOR TO STAY! appeared first on Artificial Intelligence.