Artificial Intelligence perfectly captures the zeitgeist of today’s technology. With each day progressing, we are discovering new use cases for AI. Use cases that can improve our quality of life, and also a few that threaten it if development isn’t kept in check.
Businesses from banking to healthcare are implementing AI in their operations. We’ve reached a stage wherein an AI strategy is a must-have for businesses, and a lack thereof can prove to be a serious disadvantage.
AI is here to stay, and it is revolutionizing businesses, with an impact quite similar or perhaps stronger than that of the industrial revolution.
Here are some of the latest innovations in the field:
Advanced autonomous vehicles
Autonomous vehicle manufacturers are figuring out ways to tackle complex computer vision problems. A common problem in this field has been the lack of access to edge-case training data. For example, most autonomous vehicles are only trained to identify pedestrians, road markings, other cars, and signals. But, how should such a vehicle react to a group of 4th of July celebrators storming onto the street?
With the advent of synthetic data and edge-case emphasis, computer vision systems can be trained to understand these rare exceptional cases, thus improving vehicle quality and overall safety.
Besides, computer vision systems have become better at identifying transparent objects, by a mid-step process of converting the object into its opaque version, before being fed into the system.
Improvements to public safety
The governments of nations from India to France are planning to implement computer vision cameras across streets, to curb crime. Computer vision has shown that it can be used to identify missing individuals, monitor criminal activities in real-time, and locate areas that require additional police attention.
The financial services industry has discovered the potential of Big Data and Machine Learning. Money transfer companies are discovering the importance of competitive intelligence, for increasing churn rate and exploring multiple avenues for payment-based opportunities.
Also, AI helps financial firms mitigate fraudulent transactions and provides more accurate ways of assigning credit scores.
Laws surrounding AI
Another contribution to AI’s innovation is the governments’ understanding of AI’s capabilities and potential threat. The EU announced that it will restrict the development of high-risk AI applications. The type that might hurt employment opportunities and reduce the quality of human life.
Lawmakers are beginning to understand that not all AI development will benefit society, thus allowing only the most important and life-satisfying innovations to see the light of day.
Accuracy in patent visualization
As AI innovations increase, so do the patents filed. Patent visualization platforms hold large data that can allow patent holders to identify potential users, and for tech developers to adhere to patent-related legislation.
Predictive medicine in healthcare
With the recent coronavirus outbreak, AI developers are looking for ways to use machine learning to track the spread of a virus. ML is also being used to identify health-related patterns and symptoms in patients, for implementing predictive medicine and treating avoidable diseases.
Improving workplace performance
Many factors add up to determine results at the workplace. Businesses are now using AI for inventory management, evaluating employee productivity, and identifying flaws in ongoing workflows.
AI in inventory management can be used to warn teams about shortages in resources. With the right algorithms, AI can also replace these resources on time and ensure no outages from its end. Employees can be monitored and areas of improvement, skill, and bandwidth, can be identified to make the best use of their abilities.
Music and entertainment
AI and Big Data are entering subjective fields such as the arts. Musicians can now use AI to understand trends in Billboard charting music, and even mimic traits of popular music to improve music streams and ticket sales.
Mobile gaming has benefited from augmented reality, thus creating virtual scenarios in the real world. In filmmaking, facial expressions can be adjusted and synthetic video effects can be introduced for a more vivid watching experience.
AI is developing at an incredible pace. We are seeing countries across the globe planning AI programs. An AI race between the US and China has begun, with Europe looking to give them a run for their money.
And, our understanding of AI has improved significantly, as shown by the numerous applications we have discovered for them. From deepening our technological knowledge to having an idea of AI could turn rogue, we are becoming mature advocates of AI.
The purpose of AI is to make the lives of humans simpler and add value. Hopefully, in a year from now, we’ll have even more to celebrate as AI strengthens its position as a technological juggernaut.
Artificial Intelligence a popular technology of computer science is also known as machine intelligence. Machine Learning is a systematic study of algorithms and statistical models.
AI creates intelligent machines that react like humans as it can interpret new data. ML enables computer systems to perform learning-based actions without explicit instructions.
AI global market is predicted to reach $169 billion by 2025. Artificial Intelligence will see increased investments for the implementation of advanced level software. Organizations will strategize technological advancements.
Various platforms and tools for AI and ML empower the developers to design powerful programs.
Tools for AI and ML:
Google ML Kit for Mobile:
Software development kit for Android and IOS phones enables developers to build robust applications with optimized and personalized features. This kit allows developers to ember the machine learning technologies with cloud-based APIs. This kit is integration with Google’s Firebase mobile development platform.
On-device or Cloud APIs
Face, text and landmark recognition
Detect and track object
AutoML Vision Edge
AutoML Vision Edge allows developers to train the image labeling models for over 400 categories it capacities to identify.
Smart Reply API suggests response text based on the whole conversation and facilitates quick reply.
Translation API can convert text up to 59 languages and language identification API forms a string of text to identify and translate.
Object detection and tracking API lets the users build a visual search.
Barcode scanning API works without an internet connection. It can find the information hidden in the encoded data.
Face detection API can identify the faces in images and match the facial expressions.
Image labeling recognizes the objects, people, buildings, etc. in the images and with each matched data; ML shares the score as a label to show the confidence of the system.
Custom models can grow in huge sizes.
Beta Release mode can hurt cloud-based APIs.
Smart reply is useful for general discussions for short answers like “Yes”, “No”, “Maybe” etc.
AutoML Vision Edge tool can function successfully if plenty of image data is available.
This Machine Learning framework is designed for building applications that require pattern recognition, computer vision, machine listening, and signal processing. It combines audio and image processing libraries written in C#. Statistical data processing is possible with Accord. Statistics. It can work efficiently for real-time face detection.
Algorithms for Artificial Neural networks, Numerical linear algebra, Statistics, and numerical optimization
Problem-solving procedures are available for image, audio and signal processing.
Workflow Automation, data ingestion, speech recognition,
Accord.NET libraries are available from the source code and through the executable installer or NuGet package manager.
With 35 hypothesis tests including two-way and one-way ANOVA tests, non-parametric tests useful for reasoning based on observations.
It comprises 38 kernel functions e.g. Probabilistic Newton Method.
It contains 40 non-parametric and parametric statistical distributions for the estimation of cost and workforce.
Real-time face detection
Swap learning algorithms and create or test new algorithms.
Support is available for. Net and its supported languages.
Slows down because of heavy workload.
A flexible architecture allows users to deploy computation on one or multiple desktops, servers, or mobile devices using a single API.
Runs on one or more GPUs and CPUs.
It’s yielding scheme of tools, libraries, and resources allow researchers and developers to build and deploy machine-learning applications effortlessly.
High-level APIs accedes to build and train for ML models efficiently.
Runs existing models using TensorFlow.js, which acts as a model converter.
Train and deploy the model on the cloud.
Has a full-cycle deep learning system and helps in the neural network.
You can use it in two ways, i.e. by script tags or by installing through NPM.
It can even help for human pose estimation.
It includes the variety of pre-built models and model subblocks can be used together with simple python scripts.
It is easy to structure and train your model depending on data and the models with you are training the system.
Training other models for similar activities is simpler once you have trained a model.
The learning curve can be quite steep.
It is often doubtful if your variables need to be tensors or can be just plain python types.
It restricts you from altering algorithms.
It cannot perform all computations on GPU intensive computations.
The API is not that easy to use if you lack knowledge.
This self-learning knowledge-based AI platform accumulates organizational data from people, business processes and legacy systems. It is designed to engage in complicated business tasks to forecast revenues and suggest profitable products the company can introduce.
Business Knowledge Processing
Robotic Process Automation
Organizational Transformation is possible with enhanced technologies to automate and increase operational efficiency.
It enables organizations to continually use previously gained knowledge as they grow and even modify their systems.
Faster data processing adds to the flexibility of data visualization, analytics, and intelligent decision-making.
Reduces human efforts involved in solving high-value customer problems.
It helps in discovering new business opportunities.
It is difficult to understand how it works.
Extra efforts needed to make optimum use of this software.
It has lesser features of Natural Language Processing.
Mainly it aims towards implementing and executing algorithms of statistics and mathematics. It’s mainly based on Scala and supports Python. It is an open-source project of Apache. Apache Mahout is a mathematically expressive Scala DSL (Domain Specific Language).
It is a distributed linear algebra framework and includes matrix and vector libraries.
Common maths operations are executed using Java libraries
Build scalable algorithms with an extensible framework.
Implementing machine-learning techniques using this tool includes algorithms for regression, clustering, classification, and recommendation.
Run it on top of Apache Hadoop with the help of the MapReduce paradigm.
It is a simple and extensible programming environment and framework to build scalable algorithms.
Best suited for large datasets processing.
It eases the implementation of machine learning techniques.
Run-on the top of Apache Hadoop using the MapReduce paradigm.
It supports multiple backend systems.
It includes matrix and vector libraries.
Deploy large-scale learning algorithms using shortcodes.
Provide fault tolerance if programming fails.
Needs better documentation to benefit users.
Several algorithms are missing this limits the developers.
No enterprise support makes it less attractive for users.
At times it shows sporadic performance.
It provides various algorithms and data structures for unified machine learning methods. Shogun is a tool written in C++, for large-scale learning, machine learning libraries are useful in education and research.
Huge capacity to process samples is the main feature for programs with heavy processing of data.
It provides support to vector machines for regression, dimensionality reduction, clustering, and classification.
It helps in implementing Hidden Markov models.
Provides Linear Discriminant Analysis.
Supports programming languages such as Python, Java, R, Ruby, Octave, Scala, and Lua.
It processes enormous data-sets extremely efficiently.
Link to other tools for AI and ML and several libraries like LibSVM, LibLinear, etc.
It provides interfaces for Python, Lua, Octave, Java, C#, C++, Ruby, MatLab, and R.
Cost-effective implementation of all standard ML algorithms.
Easily combine data presentations, algorithm classes, and general-purpose tools.
Some may find its API difficult to use.
It is an open-source tool for data mining and data analysis, developed in Python programming language. Scikit-Learn’s important features include clustering, classification, regression, dimensionality reduction, model selection, and pre-processing.
Consistent and easy to use API is also easily accessible.
Switching models of different contexts are easy if you learn the primary use and syntax of Scikit-Learn for one kind of model.
It helps in data mining and data analysis.
It provides models and algorithms for support vector machines, random forests, gradient boosting, and k-means.
It is built on NumPy, SciPy, and matplotlib.
BSD license lets you use commercially.
Easily documentation is available.
Call objects to change the parameters for any specific algorithm and no need to build the ML algorithms from scratch.
Good speed while performing different benchmarks on model datasets.
It easily integrates with other deep learning frameworks.
Documentation for some functions is slightly limited hence challenging for beginners.
Not every implemented algorithm is present.
It needs high computation power.
Recent algorithms such as XGBoost, Catboost, and LightGBM are missing.
Scikit learns models take a long time to train, and they require data in specific formats to process accurately.
Customization for the machine learning models is complicated.
Twitter, Facebook, Amazon, Google, Microsoft, and many other medium and large enterprises continuously use improved development tactics. They extensively use tools for AI and ML technology in their applications.
Various tools for AI and ML can ease software development and make the solutions effective to meet customer requirements. Make user-friendly mobile applications or other software that are potentially unique. Using Artificial Intelligence and Machine Learning create intelligent solutions for improved human life. New algorithm creation, using computer vision and other technology and AI training requires skills and development of innovative solutions that need powerful tools.
In the previous couple of years, computerized reasoning has progressed so rapidly that it presently appears to be not a month passes by without a newsworthy Artificial Intelligence (AI) achievement. In territories as wide-running as discourse interpretation, medicinal analysis, and interactivity, we have seen PCs beat people in frightening manners.
This has started an exchange about how AI will affect work. Some dread that as Artificial intelligence improves, it will replace laborers, making a consistently developing pool of unemployable people who can’t contend monetarily with machines. This worry, while reasonable, is unwarranted. Truth be told, AI will be the best employment motor the world has ever observed.
2020 will be a significant year in AI-related work elements, as indicated by Gartner, as AI will turn into a positive employment helper. The number of occupations influenced by Artificial Intelligence will shift by industry; through 2019, social insurance, the open division, and instruction will see constantly developing employment requests while assembling will be hit the hardest. Beginning in 2020, AI-related occupation creation will a cross into positive area, arriving at 2,000,000 net-new openings in 2025, Gartner said in a discharge.
Numerous huge advancements in the past have been related to change the time of impermanent occupation misfortune, trailed by recuperation, at that point business change and AI will probably pursue this course.
JOBS CREATED BY AI AND MACHINE LEARNING
A similar idea applies to AI. It is an instrument that individuals need to figure out how to utilize and how to apply to what’s going on with as of now. New openings are now being made that are centered around applying AI to security, improving basic AI methods, and on keeping up these new apparatuses.
Plenty of new openings will develop for those with mastery in applying center Artificial Intelligence innovation to new fields and applications. Specialists will be expected to decide the best sort of AI (for example master frameworks or AI), to use for a specific application, create and train the models, and keep up and re-train the frameworks as required. In fields, for example, security, where sellers have enabled security programming with AI, it’s up to clients – the security investigators – to comprehend the new capacities and put them to be the most ideal use.
Training is another field where AI and machine learning is making new openings. As of now, over the US, the main two situations in the rundown of scholastic openings are for Security and Machine Learning specialists. Colleges need more individuals and can’t discover educators to show these fundamentally significant subjects.
FUTURE JOBS PROSPECTS BECAUSE OF AI AND MACHINE LEARNING
In a few businesses, AI will reshape the sorts of employments that are accessible. What’s more, much of the time, these new openings will be more captivating than the monotonous errands of the past. In assembling, laborers who had recently been attached to the generation line, looking for blemished items throughout the day, can be redeployed in increasingly profitable interests — like improving procedures by following up on bits of knowledge gathered from AI-based sensor and vision stages.
These are increasingly specific errands and retraining or uptraining might be important for laborers to successfully satisfy these new jobs — something the two organizations and people should address sooner than later.
Man-made intelligence-based arrangements in any industry produce monstrous measures of information, frequently from heterogeneous sources. Successfully saddling the intensity of this information requires human abilities. Profound learning researchers have come to comprehend that setting is basic for preparing powerful AI models — and people are important to clarify this information to give set in uncertain circumstances and help spread all this present reality varieties an AI framework will experience.
Keeping that in mind, Appen utilizes more than 40,000 remote contractual workers a month to perform information explanation for our customers, drawing from a pool of more than 1 million talented annotators around the world.
These occupations wouldn’t exist without the profound learning innovation that makes AI conceivable. As researchers and designers make immense advances in innovation, organizations and laborers may need to adopt new mechanical aptitudes to remain aggressive.
Simulated intelligence is helping drive work creation in cybersecurity
As the worldwide economy is progressively digitized and mechanized, effectively unavoidable criminal ventures – programmers, malware, and different dangers – will develop exponentially, requiring engineers, analyzers, and security specialists to alleviate dangers to crucial open framework and meet expanding singular personality concerns.
In the previous couple of years there has been an enormous increment in cybersecurity work postings, a large number of which stay unfilled. With this deficiency of cybersecurity experts, most security groups have less time to proactively protect against progressively complex dangers. This interest has made a significant specialty for laborers to fill.
The stream down impact of industry-wide digitalization
In a roundabout way, the efficiencies and openings that profound learning and computerization empower for organizations can make a great many employments. While mechanized conveyance strategies, for example, self-driving conveyance trucks will take a great many drivers off the street, an ongoing Strategy + Business article proposes that, “In reality as we know it where organizations are progressively made a decision on the nature of the client experience they give, you will require representatives who can consolidate the aptitudes of a client care specialist, advertiser, and sales rep to sit in those trucks and connect with clients as they make conveyances.”
Additionally, the higher profitability and positive development empowered by AI will positively affect employing as organizations will just need to procure more laborers to take on existing assignments that require human abilities. Consider client support, publicists, program administrators, and different jobs that require abilities, for example, compassion, moral judgment, and inventiveness.
Growing new aptitudes to endure and flourish
It’s anything but difficult to perceive any reason why laborers and administrators the same may be hesitant to execute AI-controlled mechanization. Be that as it may, as their rivals receive this innovation and start to outpace them in deals, creation, and development, it will expect them to adjust. The two organizations and laborers should put resources into developing new innovative aptitudes to enable them to remain significant in this information-driven scene. If they can do this, the open doors for business and expert development are perpetual.
DEVELOPMENT IN THE FIELD OF AI and ML
Man-made reasoning is a method for making a PC, a PC controlled robot, or a product think keenly, in the comparative way the insightful people think. Man-made brainpower is a science and innovation dependent on orders, for example, Computer Science, Biology, Psychology, Linguistics, Mathematics, and Engineering. A significant push of Artificial Intelligence (AI) is in the advancement of PC capacities related to human knowledge, for example, thinking, learning, and critical thinking.
AI is a man-made consciousness-based method for creating PC frameworks that learn and advance dependent on experience. Some basic AI applications incorporate working self-driving autos, overseeing speculation reserves, performing legitimate disclosure, making therapeutic analyses, and assessing inventive work. A few machines are in any event, being educated to mess around.
Man-made intelligence and MACHINE LEARNING isn’t the eventual fate of innovation — it’s nowhere. Simply see how voice aides like Google’s Home and Amazon’s Alexa have turned out to be increasingly more unmistakable in our lives. This will just proceed as they adapt more aptitudes and organizations work out their associated gadget biological systems. The accompanying can be viewed as a portion of the significant advancements in the field of AI.
Artificial intelligence in Banking and Payments
This report features which applications in banking and installments are most developed for AI. It offers models where monetary organizations (FIs) and installments firms are as of now utilizing the innovation, talks about how they should approach actualizing it, and gives depictions of merchants of various AI-based arrangements that they might need to think about utilizing.
Computer-based intelligence in E-Commerce
This report diagrams the various uses of AI in retail and gives contextual analyses of how retailers are increasing a focused edge utilizing this innovation. Applications incorporate customizing on the web interfaces, fitting item suggestions, expanding the hunt significance, and giving better client support.
Computer-based intelligence in Supply Chain and Logistics
This report subtleties the variables driving AI appropriation in-store network and coordinations, and looks at how this innovation can decrease expenses and sending times for activities. It likewise clarifies the numerous difficulties organizations face actualizing these sorts of arrangements in their store network and coordinations tasks to receive the rewards of this transformational innovation.
Artificial intelligence in Marketing
This report talks about the top use cases for AI in advertising and looks at those with the best potential in the following couple of years. It stalls how promoting will develop as AI robotizes medicinal undertakings, and investigates how client experience is winding up increasingly customized, pertinent, and auspicious with AI.
To close, AI introduces a colossal open door for venturesome individuals. Representatives have the chance to jump into another field and conceptual their business to another, more significant level of investigation and vital worth. Businesses need to help these moves and for the most part remain open to representatives rethinking themselves as they hold onto innovations, for example, AI.