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Machine Learning

What is Machine Learning?

Machine learning (ML) is fundamentally a subset of artificial intelligence (AI) that allows the machine to learn automatically. No explicit programs are needed instead of coding you gather data and feed it to the generic algorithm. It is a scientific study of algorithms and statistical models used by computers to perform specific tasks.

The machine builds a logic based on that data. It can access data and teach itself from various instructions, interactions, and queries resolved. ML forms data patterns that help in making better decisions. The machines learn without human interference even in fields where developing a conventional algorithm is not workable. ML includes data mining, data analysis to perform predictive analytics.

Machine learning facilitates the analysis of substantial quantities of data. It can identify profitable opportunities, risks, returns and much more at a very high speed and accuracy. Costs and resources are involved in training the agent to process large volumes of information gathered.

Working of Machine Learning:

Machine Learning algorithm obtains skill by using the training data and develops the ability to work on various tasks. It uses data for accurate predictions. If the results are not satisfactory, we can request it to produce other alternative suggestions. ML can have supervised, semi-supervised, unsupervised or reinforcement learning.

Supervised learning is the machine is trained by the dataset to predict and take decisions. The machine applies this logic to the new data automatically once learned. The system can even suggest new input after adequate training and can even compare the actual output with the intended output. This model learns through observations, corrects the errors by altering the algorithm. The model itself finds the patterns and relationships in the dataset to label the data. It finds structures in the data to form a cluster based on its patterns and uses to increase predictability.

Semi-supervised learning uses labeled and unlabelled data for the training purpose. This is partly supervised machine learning, and it considers labeled data in small quantities and unlabelled data in large quantity. The systems can improve the learning accuracy using this method. If the companies have acquired and labelled data; have skilled and relevant resources in order to train it or learn from it they choose semi-supervised learning.

Unsupervised machine learning algorithms are useful when the information used to train is not classified or labeled. Studies that include unsupervised learning prove how systems can conclude a function to depict a hidden structure from the unlabelled data. The system explores data supposition to describe the obscure structures from the unlabelled data.

Reinforcement machine learning, these algorithms can interact with its environment by generating actions. It can find the best outcome from some trial and errors and the agent earns reward or penalty points to maximize its performance. The model trains itself to predict the new data presented. The reinforcement signal is a must for the agent to find out the best action from the ones its suggestions.

Future of ML

Evolution of Machine Learning:

Machine learning has evolved over a period and experiences continuous growth. It developed the pattern recognition and non-programmed automated learning of computers to perform simple and complex tasks. Initially, the researchers were curious about whether computers can learn with the least human intervention just with the help of data. The machines learn from the previous methods of computations, statistical analysis and can repeat the process for other datasets. It can recommend the users for the product and services, respond to FAQs, notify for subjects of your choice, and even detect fraud.

Machine Learning as of today:

Machine Learning has gained popularity for its data processing and self-learning capacity. It is involved in technological advancements and its contribution to human life is noteworthy. E.g. Self-driving vehicles, robots, chatbots in the service industry and innovative solutions in many fields.

Currently, ML is widely used in :

1. Image Recognition: ML algorithms detect and recognize objects, human faces, locations and help in image search. Facial recognition is widely used in mobile applications such as time punching apps, photo editing apps, chats, and other apps where user authentication is mandatory.

2. Image Processing: Machine learning conducts an autonomous vision useful to improve imaging and computer vision systems. It can compress images and these formats can save storage space, transmit faster. It maintains the quality of images and videos.

3. Data Insights: The automation, digitization, and various AI tools used by the systems provide insights based on an organization’s data. These insights can be standard or customized as per the business need.

4. Market Price: ML helps retailers to collect information about the product, its features, its price, promotions applied, and other important comparatives from various sources, in real-time. Machines convert the information to a usable format, tested with internal and external data sources, and the summary is displayed on the user dashboard. The comparisons and recommendations help in making accurate and beneficial decisions for the business.

5. User Personalisation: It is one of the customer retention tactic used in all the sectors. Customer expectations and company offerings have a commercial aspect attached; hence, personalization is introduced on a wide variety of forms. ML processes massive data of customers such as their internet search, personal information, social media interactions, and preferences stored by the users. It helps companies increase the probability of conversion and profitability with reduced efforts with ML technology. It can help branding, marketing, business growth and improve performance.

6. Healthcare Industry: Machine learning assists to improve healthcare service quality; reduce costs, and increase satisfaction. ML can assist medical professionals by searching the relevant data facts and suggest the latest treatments available for such illnesses. It can suggest the precautionary measures to the patient for better healthcare. AI can maintain patient data and use it as a reference for critical cases in hospitals across the globe. The machines can analyze images of MRI or CT Scan, process clinical procedures videos, check laboratory results, sort patient information and use efficiently. ML algorithms can even identify skin cancer and cancerous tumors by studying mammograms.

7. Wearables: These wearables are changing patient care, with strong monitoring of health as a precaution or prevention of illness. They track the heart rate, pulse rate, oxygen consumption by the muscles and blood sugar level in real-time. It can reduce the chances of heart attack or injury, and can recommend the user for medicine dose, health check-up, type of treatment, and help the faster recovery of the patient. With an enormous amount of data that gets generated in healthcare, the reliance on machine learning is unavoidable.

8. Advanced cybersecurity: Security of data, logins, and personal information, bank and payment details is necessary. The estimated losses that organizations face because of cybercrime are likely to reach $6 trillion yearly. Threat is raising the cybersecurity costs and increasing the burden on the operational expenses of organizations. The ML implementation protects user data, their credentials, saves from phishing attacks and maintains privacy.

9. Content Management: The users can see sensible content on their social media platforms. The companies can draw the attention of the target audience and it reduces their marketing and advertising costs. Based on human interactions these machines can show relevant content.

10. Smart Homes: ML does all mundane tasks for you, maintaining the monthly grocery, cleaning material, and regular purchase lists. It can update the list when there are input and order material on the scheduled date. It increases the security at home by keeping the track of known visitors and barring the other from entering the premise or specifies suspicious activities.

11. Logistics: Machine learning can keep track of the user’s choices for delivery and can suggest based on the instructions and addresses they use often. The confirmations, notifications, and feedback about the delivery is processed by the machines more efficiently and in real-time.

Future of ML:

Do not be surprised if we are found learning dance, music, martial arts, and academic subjects from the Bots. We will shortly experience improved services in travel, healthcare, cybersecurity, and many other industries as the algorithms can run throughout with no break, unlike humans. They not only deal but respond and collect feedback in real-time.

Researchers are developing innovative ways of implementing machine-learning models to detect fraud, defend cyberattacks. The future of transportation is great with the wide-scale adoption of autonomous vehicles.

The voice, sound, image, and face recognition, NLP is creating a better understanding of customer requirements and can serve better through machine learning.

Autonomous Vehicles like self-driving cars can reduce traffic-related problems like accidents and keep the driver safe in case of a mishap. ML is developing powerful technologies to let us operate these autonomous vehicles with ease and confidence. The sensors use the data points to form algorithms that can lead to safe driving.

Deeper personalization is possible with ML as it highlights the possibilities of improvement. The advertisements will be of user choice as more data is available from the collective response of each user for the text or video they see.

The future will simplify the machine learning by extracting data from the devices directly instead of asking the user to fill the choices. The vision processing lets the machine view and understands the images in order to take action.

You can now expect cost-effective and ingenious solutions that will alter your choices and change your set of expectations from the companies and products.

According to the survey by Univa 96% of companies think there will be outbursts in Machine Learning projects by 2020. Two out of ten companies have ML projects running in production. 93% of companies, which participated in the survey, have commenced ML projects. (344 Technology and IT professionals were part of survey)

Approximately 64% of technology companies, 52% of the finance sector, 43% of healthcare, 31% of retail, telecommunications, and manufacturing companies are using ML and overall 16 industries are already using machine-learning processes.

Final Thoughts:

Machine Learning is building a new future that brings stability to the business and eases human life. Sales data analysis, streamlining data, mobile marketing, dynamic pricing, and personalization, fraud detection, and much more than the technology has already introduced, we will see new heights of technology.

Artificial Intelligence Applications

Man-made brainpower has significantly changed the business scene. What began when in doubt based mechanization is currently fit for copying human communication. It isn’t only the human-like abilities that make man-made consciousness extraordinary.

A propelled AI calculation offers far superior speed and unwavering quality at a much lower cost when contrasted with its human partners Artificial insight today isn’t only a hypothesis. It, indeed, has numerous viable applications. A 2016 Gartner research demonstrates that by 2020, at any rate, 30% of organizations universally will utilize AI, in any event, one piece of their business forms.

Today businesses over the globe are utilizing computerized reasoning to advance their procedure and procure higher incomes and benefits. We contacted some industry specialists to share their point of view toward the uses of man-made reasoning. Here are the experiences we have gotten: 

What is AI?

Computerized reasoning, characterized as knowledge shown by machines, has numerous applications in the present society. Simulated intelligence has been utilized to create and propel various fields and enterprises, including money, medicinal services, instruction, transportation, and the sky is the limit from there. 

Man-made knowledge systems will typically indicate most likely a part of the going with practices related to human understanding: orchestrating, getting the hang of, thinking, basic reasoning, learning depiction, perception, development, and control and, to a lesser degree, social information and creative mind. 

Applications of Artificial Intelligence for business

Human-made intelligence is omnipresent today, used to suggest what you should purchase next on the web, to comprehend what you state to menial helpers, for example, Amazon’s Alexa and Apple’s Siri, to perceive who and what is in a photograph, to spot spam, or recognize Mastercard extortion. 

Utilization of Artificial Intelligence in Business 

• Improved client administrations. 

In the event that you run an online store, you’ve absolutely seen a few changes in client conduct. 30% of every single online exchange presently originate from portable. Despite the fact that cell phone proprietors invest 85% of their versatile energy in different applications, just five applications (counting delivery people and web-based life) hold their consideration.

So as to empower versatile application selection, the world’s driving retailers like Macy’s and Target introduce signals and go to gamification. Facebook and Kik went significantly further and propelled chatbot stages. A chatbot (otherwise known as “bot” or “chatterbot”) is a lightweight AI program that speaks with clients the manner in which a human partner would.

Despite the fact that H&M, Sephora and Tesco were among the principal organizations to get on board with the chatbot fleeting trend, bots’ potential stretches a long way past the web-based business area. The Royal Dutch Airlines have constructed a Facebook bot to assist voyagers with registration docs and send notices on flight status.

Taco Bell built up a menial helper program that oversees arranges through the Slack informing application. HP’s Print Bot empowers clients to send records to the printer directly from Facebook Messenger.

As per David Marcus, VP of informing items at Facebook, 33 thousand organizations have just constructed Facebook bots — and now they’re “beginning to see great encounters on Messenger”; 

• Workload computerization and prescient support. 

By 2025, work mechanization will prompt an overall deficit of 9.1 million US employments. In any case, AI applications won’t cause the following work emergency; rather, savvy projects will empower organizations to utilize their assets all the more viably. Engine, an electric firm from France, utilizes rambles and an AI-controlled picture preparing application to screen its foundation.

The London-based National Free Hospital joined forces with DeepMind (an AI startup claimed by Google) to create calculations distinguishing intense kidney wounds and sight conditions with next to zero human impedance. General Electric battles machine personal time by gathering and breaking down information from savvy sensors introduced on its hardware. On account of the Internet of Things and technology, organizations can lessen working costs, increment profitability and inevitably make a learning-based economy; 

• Effective information the executives and examination.

 Before the current year’s over, there will be 6.4 billion associated contraptions around the world. As more organizations start utilizing IoT answers for business purposes, the measure of information produced by savvy sensors increments (and will arrive at 400 zettabytes by 2018). On account of Artificial Intelligence, we can come this information down to something significant and increase superior knowledge into resources and workforce the board.

The LA-based startup built up an AI application that sweeps a client’s internet-based life presents on recognize unsuitable substances (bigotry, savagery, and so forth.). About 43% of organizations get to potential workers’ online life profiles. Presently you can confide in the undertaking to a savvy calculation and spare your HR’s time (especially as a human wouldn’t locate a bigot tweet posted two years prior); 

• Evolution of showcasing and publicizing.

New innovations have changed the manner in which advertisers have been working for a considerable length of time. Utilizing the AI Wordsmith stage, you can have a news story composed (or created!) in negligible seconds. The cunning Miss Piggy bot talks away with fans to advance the Muppet Show arrangement. Facebook uses AI calculations to follow client conduct and improve advertisement focusing on.

Airbnb has built up a shrewd application to upgrade settlement costs considering the hotel’s area, regular interest, and well-known occasions held close by. With Artificial Intelligence, advertisers can computerize an incredible portion of routine errands, obtain significant information and commit more opportunity to their center duties — that is, expanding incomes and consumer loyalty.

Applications of Artificial Intelligence for Business

1. Media and web-based business 

Some AI applications are equipped towards the investigation of varying media substances, for example, motion pictures, TV programs, ad recordings or client produced content. The arrangements regularly include PC vision, which is a noteworthy application region of AI. 

Ordinary use case situations incorporate the examination of pictures utilizing object acknowledgment or face acknowledgment procedures, or the investigation of video for perceiving important scenes, articles or faces. The inspiration for utilizing AI-based media and technology can be in addition to other things the assistance of media search, the making of a lot of enlightening watchwords for a media thing, media content approach observing, (for example, confirming the appropriateness of substance for a specific TV review time), discourse to content for chronicled or different purposes, and the discovery of logos, items or big-name faces for the situation of significant notices.

AI applications are additionally generally utilized in E-trade applications like visual hunt, chatbots, and technological tagging. Another conventional application is to build search discoverability and making web-based social networking content shoppable. 

2. Market Prediction 

We are utilizing AI in various conventional spots like personalization, natural work processes, upgraded looking and item suggestions. All the more as of late, we began preparing AI into our go-to-showcase activities to be first to advertise by anticipating what’s to come. Or on the other hand, would it be advisable for me to state, by “attempting” to anticipate what’s to come? Google search is presently upgraded with AI calculations giving clients significant substance — and that is one reason why customary SEO is gradually biting the dust.

3. Foreseeing Vulnerability Exploitation 

We’ve as of late begun utilizing AI to anticipate if a weakness in a bit of programming will wind up being utilized by aggressors. This enables us to remain days or weeks in front of new assaults. It’s an enormous extension issue, yet by concentrating on the straightforward arrangement of “will be assaulted” or “won’t be assaulted,” we’re ready to prepare exact models with high review. 

4. Controlling Infrastructure, Solutions, and Services 

We’re utilizing AI/ML in our cooperation arrangements, security, administrations, and system foundation. For instance, we as of late obtained an AI stage to manufacturing conversational interfaces to control the up and coming age of talk and voice aides. We’re additionally including AI/ML to new IT administrations and security, just as a hyper-joined framework to adjust the outstanding burdens of processing frameworks. 

5. Cybersecurity Defense 

Notwithstanding conventional safety efforts, we have received AI to help with the cybersecurity barrier. The AI framework continually breaks down our system parcels and maps out what is typical traffic. It knows about more than 102,000 examples on our system. The AI prevails upon customary firewall standards or AV information in that it works consequently without earlier mark learning to discover irregularities. 

6. Human services Benefits 

We are investigating AI/ML innovation for human services. It can help specialists with findings and tell when patients are breaking down so restorative intercession can happen sooner before the patient needs hospitalization. It’s a successful win for the social insurance industry, sparing expenses for both the emergency clinics and patients. The exactness of AI can likewise identify infections, for example, malignant growth sooner, hence sparing lives. 

7. Shrewd Conversational Interfaces 

We are utilizing AI and AI to manufacture smart conversational chatbots and voice abilities. These AI-driven conversational interfaces are responding to inquiries from habitually posed inquiries and answers, helping clients with attendant services in inns, and to give data about items to shopping. Headways in profound neural systems or profound learning are making a considerable lot of these AI and ML applications conceivable. 

8. Showcasing and man-made brainpower 

The fields of advertising and man-made consciousness unite in frameworks that aid territories, for example, showcase gauging, and mechanization of procedures and basic leadership, alongside expanded effectiveness of undertakings which would, as a rule, be performed by people. The science behind these frameworks can be clarified through neural systems and master frameworks, PC programs that procedure input and give profitable yield to advertisers. 

Man-made consciousness frameworks originating from social figuring innovation can be applied to comprehend interpersonal organizations on the Web. Information mining procedures can be utilized to dissect various kinds of interpersonal organizations. This examination encourages an advertiser to distinguish persuasive entertainers or hubs inside systems, data which would then be able to be applied to adopt a cultural promoting strategy. 

Conclusion

AI applications, systems, and technology can’t copy innovativeness or keenness. Nonetheless, it can remove the overwhelming work trouble with the goal that advertisers can focus on key arranging and innovativeness. Almost certainly, in not so distant future we will run over such huge numbers of versatile applications that will be fabricated utilizing most recent AI innovations and they will have an incredible capacity to make this world considerably more intelligent.

Development of artificial intelligence - a brief history | Blog | Bridged.co

The Three Laws of Robotics — Handbook of Robotics, 56th Edition, 2058 A.D.
1. First Law — A robot may not injure a human being or, through inaction, allow a human being to come to harm.
2. Second Law — A robot must obey the orders given it by human beings except where such orders would conflict with the First Law.
3. Third Law — A robot must protect its own existence as long as such protection does not conflict with the First or Second Laws.

Ever since Isaac Asimov penned down these fictional rules governing the behavior of intelligent robots — in 1942 — humanity has become fixated with the idea of making intelligent machines. After British mathematician Alan Turing devised the Turing Test as a benchmark for machines to be considered sufficiently smart, the term artificial intelligence was coined in 1956 at a summer conference in Dartmouth University, USA for the first time. Prominent scientists and researchers debated the best approaches to creating AI, favoring one that begins by teaching a computer the rules governing human behavior — using reason and logic to process available information.

There was plenty of hype and excitement about AI and several countries started funding research as well. Two decades in, the progress made did not deliver on the initial enthusiasm or have a major real-world implementation. Millions had been spent with nothing to show for it, and the promise of AI failed to become anything more substantial than programs learning to play chess and checkers. Funding for AI research was cut down heavily, and we had what was called an AI Winter which stalled further breakthroughs for several years.

Gary Kasparov vs IBM Deep blue | Blog | Bridged.co

Programmers then focused on smaller specialized tasks for AI to learn to solve. The reduced scale of ambition brought success back to the field. Researchers stopped trying to build artificial general intelligence that would implement human learning techniques and focused on solving particular problems. In 1997, for example, IBM supercomputer Deep Blue played and won against the then world chess champion Gary Kasparov. The achievement was still met with caution, as it showcased success only in a highly specialized problem with clear rules using more or less just a smart search algorithm.

The turn of the century changed the AI status quo for the better. A fundamental shift in approach was brought in that moved away from pre-programming a computer with rules of intelligent behavior, to training a computer to recognize patterns and relationships in data — machine learning. Taking inspiration from the latest research in human cognition and functioning of the brain, neural network algorithms were developed which used several ‘nodes’ that process information similar to how neurons do. These networks have multiple layers of nodes (deep nodes and surface nodes) for different complexities, hence the term deep learning.

Representation of neural networks | Blog | Bridged.co

Different types of machine learning approaches were developed at this time:

Supervised Learning uses training data which is correctly labeled to teach relationships between given input variables and the preferred output.

Unsupervised Learning doesn’t have a training data set but can be used to detect repetitive patterns and styles.

Reinforcement Learning encourages trial-and-error learning by rewarding and punishing respectively for preferred and undesired results.

Along with better-written algorithms, several other factors helped accelerate progress:

Exponential improvements in computing capability with the development of Graphical Processing Units (GPUs) and Tensor Processing Units have reduced training times and enabled implementation of more complex algorithms.

Data repositories for AI systems | Blog | Bridged.co

The availability of massive amounts of data today has also contributed to sharpening machine learning algorithms. The first significant phase of data creation happened with the spread of the internet, with large scale creation of documents and transactions. The next big leap was with the universal adoption of smartphones generating tons of disorganized data — images, music, videos, and docs. We have another phase of data explosion today with cloud networks and smart devices constantly collecting and storing digital information. With so much data available to train neural networks on potential scores of use-cases, significant milestones can be surpassed, and we are now witnessing the result of decades of optimistic strides.

  • Google has built autonomous cars.
  • Microsoft used machine learning to capture human movement in the development of Kinect for Xbox 360.
  • IBM’s Watson defeated previous winners on the television show Jeopardy! where contestants need to come up with general knowledge questions based on given clues.
  • Apple’s Siri, Amazon’s Alexa, Google Voice Assistant, Microsoft’s Cortana, etc. are well-equipped conversational AI assistants that process language and perform tasks based on voice commands.
Developments in AI | Blog | Bridged.co
  • AI is becoming capable of learning from scratch the best strategies and gameplay to defeat human players in multiple games — Chinese board game Go by Google DeepMind’s AlphaGo, computer game DotA 2 by OpenAI are two prolific instances.
  • Alibaba language processing AI outscored top contestants in a reading and comprehension test conducted by Stanford University.
  • And most recently, Google Duplex has learned to use human-sounding speech almost flawlessly to make appointments over the phone for the user.
  • We have even created a Chatbot (called Eugene Goostman) that passed the Turing Test, 64 years after it was first proposed.

All the above examples are path-breaking in each field, but they also show the kind of specialized results that we have managed to attain. In addition, such achievements were realized only by organizations which have access to the best resources — finance, talent, hardware, and data. Building a humanoid bot which can be taught any task using a general artificial intelligence algorithm is still some distance away, but we are taking the right steps in that direction.

Bridged's service offerings | Blog | Bridged.co

Bridged is helping companies realize their dream of developing AI bots and apps by taking care of their training data requirements. We create curated data sets to train machine learning algorithms for various purposes — Self-driving Cars, Facial Recognition, Agri-tech, Chatbots, Customer Service bots, Virtual Assistants, NLP and more.