With the sudden technological boom within the IT and development organizations a couple of years ago, both Artificial Intelligence (AI) and Machine Learning have now become the trending careers for a lot of people to follow with so many businesses coming up and clamoring for the best new talent, since it has been established that AI is rapidly transforming every sphere of our life there are certain key AI technologies to focus on so that one can take machine learning projects to the next level.
Artificial intelligence and machine learning are among the most significant technological developments in recent history.
What is Machine Learning?
Machine learning is an application of Artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
The process of learning begins with observations or data, such as, direct experience, or instruction, to look for patterns in data and make better decisions in the future based on the examples that we provide.
The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly. Machine learning is a trending topic in research and industry, with new methodologies developed all the time. The speed and complexity of the field makes keeping up with new techniques difficult even for experts — and potentially overwhelming for beginners.
How different Businesses/Organisations use ML and AI ?
1. Yelp — Image Curation at Scale
Yelp’s machine learning algorithms help the company’s human staff to compile, categorize, and label images more efficiently
2. Pinterest — Improved Content Discovery
Machine learning touches virtually every aspect of Pinterest’s business operations, from spam moderation and content discovery to advertising monetization and reducing churn of email newsletter subscribers.
3. Facebook — Chatbot Army
AI applications are being used at Facebook to filter out spam and poor-quality content
4. Twitter — Curated Timelines
Twitter’s AI evaluates each tweet in real time and “scores” them according to various metrics.
Ultimately, Twitter’s algorithms then display tweets that are likely to drive the most engagement. This is determined on an individual basis; Twitter’s machine learning tech makes those decisions based on your individual preferences
5. Google — Neural Networks and ‘Machines That Dream’
According to Google, the company is researching “virtually all aspects of machine learning,” which will lead to exciting developments in what Google calls “classical algorithms” as well as other applications including natural language processing, speech translation, and search ranking and prediction systems.
6. Edgecase — Improving E commerce Conversion Rates
Leverage its tech to provide a better experience for shoppers who may only have a vague idea of what they’re looking for
7. Baidu — The Future of Voice Search
8. Hubspot — Smarter Sales
9. IBM — Better Healthcare
Watson has been deployed in several hospitals and medical centers in recent years. Watson also shows significant potential in the retail sector, where it could be used as an assistant to help shoppers,
10. Salesforce — Intelligent crms
Salesforce Einstein allows businesses that use Salesforce’s CRM software to analyze every aspect of a customer’s relationship.
Advantages of ML and AI :
· Real-Time Business Decision Making
· Eliminating Manual Tasks
· Enhancing Security and Network Performance
· Improved Business Models and Services
· Reducing Operating Expense
· Automation of Cyber attack Countermeasures
· Convincing Generative Models
· Better Machine Learning Training
CASE STUDY
Artificial intelligence in the field of security and surveillance:
The need for increased monitoring and protection has led to rapid advancements in the sphere of security and surveillance technology today. Statistics reveal that the worldwide expenditure on information security products reached over $114 billion by the last year. New and more innovative solutions are being launched in the market to dabble with the crisis of security at every level. Artificial intelligence for surveillance and security is another one of AI’s many life-altering virtues.
How does it work?
AI for video surveillance and security uses machine-based learning and algorithm to monitor and analyze the images, videos, and data recorded from the video surveillance cameras. It is also capable of recognizing and dissecting the movement of human beings, vehicles and a wide array of objects.
AI can make use of machine-based vision to sort the stored data and send alerts on the non-recognition of the system indicating the user of trespassing. The AI software has the ability to keep a record of the surveillance of hundreds and thousands of cameras thereby, challenging and withstanding the ability of us humans to do the same. With algorithms and deep learning, AI can identify the slightest of changes in the normal behavior of a network and can prevent potential attacks.
Artificial intelligence in security applications:
- Cyber Attacks (Defense Against Hackers) and Software Errors/Failures
The software that powers our computers and smart devices is subject to error in code, as well as security vulnerabilities that can be exploited by human hackers. AI systems can be trained to identify even the smallest behaviors of ransomware and malware attacks before it enters the system and then isolate them from that system. They can also use predictive functions that surpass the speed of traditional approaches.
Biometric logins are increasingly being used to create secure logins by either scanning fingerprints, retinas, or palm prints. AI systems can also be used in situations of multi-factor authentication to provide access to their users. Multi-factor authentication collects user information to understand the behavior of this person and make a determination about the user’s access privileges.
2) Security & Crime Prevention
There has always been a war between the governments and criminals of a country. Although the crime rate has shown a decline in many countries, criminal activities have not been tackled efficiently There are many technologies that can help police to reduce crime, and AI is one of them.
· Detecting gunfire — Sensors can be installed in the city infrastructure. The sensors will be connected to a cloud-based application that can reliably detect and accurately locate the gunfire. Every sensor captures the time and the sound of gunfire. This data from multiple sensors can help locate the incident. Sensors can also help determine the position of the shooter. The overall information is then sent to the police headquarters with the precise location of the gunfire.
· Detecting clues on the crime scene
· Detecting bombs — With the ability to identify bomb components, AI-enabled robots can easily detect bombs without risking the lives of security personnel to detect them.
· AI for crime prevention:
· Predicting crime spots
· Predicting who will commit a crime
· Deciding pretrial release
3) Privacy Protection
· AI can efficiently analyze user behaviors, deduce a pattern, and identify all sorts of abnormalities or irregularities in the network. With such data, it’s much easier to identify cyber vulnerabilities quickly. AI bots can provide a “privacy concierge” function in which they can recognize, route and service privacy data requests faster and more cheaply than humans, AI has already shown itself to be highly effective at identifying and classifying data that could take a human operator’s significant time and effort to review. This means that much of the existing data businesses hold that could fall within privacy regulations (and therefore need to be available to consumers on request) can be identified and aggregated by ais doing continual sweeps through disparate data stores. AI can also provide a role in handling sensitive data itself. Specifically, tasks in which sensitive data might be exposed to a human operator unnecessarily.
This means that ais could, in the near future, be used to handle much larger amounts of sensitive data in ways that remove humans from the chain, and thus simplify the process of keeping that data secure
The 2019 Gartner Security and Risk Survey, which was conducted from March 2019 through April 2019, showed that over 40% of privacy compliance technology will rely on AI by 2023, up from 5% in 2019.
Thanks for reading!
