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Tag: future of artificial intelligence

The future of artificial intelligence and machine learning in the Biosciences

Posted on by Imarticus Learning

Do you know why artificial intelligence courses are so popular? For the last 70 to 80 years, we have been trying to simulate our intelligence in many artificial entities, which has given rise to the growing field of artificial intelligence (AI). Although AI has surpassed humans in many respects, it still does not live up to its name. AI, as we define it, does not yet exist, nor is there a consensus among experts as to whether it can be achieved.

However, while AI is captivating with its incredible applications and rapid growth (autonomous cars, nanorobots, etc), AI has infiltrated almost all disciplines and has had a particular impact on biosciences. AI offers sufficient computational power and capacity to address the complexity of biological research through simulations (known as “artificial life”). It presents itself as an ideal testing ground, a bounded but unbounded environment where physical laws are adaptable, all parameters are traceable, measurable, storable and retrievable.

AI in Biology

This translates into the possibility of overcoming some of the most important challenges of research in biology. For example, the ethical limits of animal experimentation with drugs for cancer and other diseases, or the methodological difficulties in studying complex systems such as human language, multicellularity or collective intelligence. AI also benefits from this interaction. After all, the key to being able to reproduce a natural system in an artificial environment depends on the knowledge one has of the system in question.

Deep Learning

Deep Learning is one of the many approaches to AI and is inspired by the structure and functioning of the brain through the interconnection of neurons, mimicking the biological structure of the brain through algorithms called Artificial Neural Networks that specialise in detecting specific features, through different layers of neurons, to achieve unsupervised learning. The concept is given by the multiple layers it can comprise.

A neural network needs approximately 50,000 times more energy to function than the human brain. For this reason, computers with traditional architectures are not suited to support the parallel processing that the brain carries out so efficiently. Therefore, research is being carried out into brain-mimicking computing techniques called Neuromorphic Computing.

Artificial Immune Systems

There is an initiative that aims to understand how different parts of the brain work in order to diagnose and treat brain diseases and to develop neuromorphic computers that can learn in the same way as the brain does. These advances need to incorporate multidisciplinary knowledge from neuroscience research, psychology, and ICTs. But it is not only the human brain that is a source of inspiration. Artificial Immune Systems comprise computational methods based on the processes and mechanisms of the human immune system and are used for learning and protecting information systems from malware.

AI and IOT

Finally, we could compare the relationship between Artificial Intelligence and the Internet of Things as the relationship between the brain and the human body. Our bodies collect sensory information (sight, hearing, touch, etc) and send it to the brain, to make sense of this information in order to make the decisions and/or actions, sending signals back to our body if necessary, for example, to pick up an object.

Conclusion

In conclusion, the symbiotic relationship between AI and bioscience has provided the ultimate testing ground for solving some mysteries of biology, as well as the theoretical framework needed to achieve real artificial intelligence. Any of us can learn AI or do a machine learning certification, but only the best prepared will be part of this amazing field of study, so study with Imarticus and go as far as you want.

How Machine Learning is Changing Identity Theft Detection?

Posted on by Imarticus Learning

 

Debilitating data breaches and identity theft scenarios have left several high-profile firms across the globe scrambling to recover losses. In 2018 alone, in the US, over $1.48 billion worth of losses occurred, after 1.4 million fraud reports1. Of these reports, identity theft was a significant defining factor. Businesses and corporates alike are turning to machine learning and Artificial Intelligence (AI) in general for help. Current employees are also being upskilled for an artificial intelligence career through machine learning courses in order to prep for the future of machine learning.

Machine learning has already permeated everyday lives, from online recommendations on your favorite streaming site to self-drive cars that have awed the masses. When it comes to identity theft detection, machine learning has so much potential– especially since there are larger players and higher factors at stake.

Here are some ways in which AI and machine learning are being leveraged to detect, reduce and prevent identity theft:

Authentication Tests

With machine learning, identity documents including the likes of passports, drivers’ licenses, and PAN cards are scanned and cross-verified with an unseen database in real-time. An additional set of authentication tests can usurp theft to some extent– the use of biometrics and facial recognition being some of the more used ML-based tests. Other examples of authentication tests include microprint tests, OCR-barcode-magnetic strip cross-verification, and paper-and-ink validation2.

Real-Time Decision Making

Machine learning training has the power to operationalize and automate the process of data analytics, especially tasks that are mundane or prone to human error. Beyond speeding up the process of identity theft detection, machine learning enables real-time decision making to stop theft in its tracks or sound an alert in case of a potential threat. This is a boon for businesses both large and small who cannot afford to waste valuable human resources on mundane tasks. By detecting identity theft at speeds hitherto unmatched, machine learning allows analysts to make spot decisions before any damage is done.

Pattern Identification

An added benefit of using machine learning to revolutionize identity theft detection is pattern recognition. Since any machine learning algorithm is wired to a database with tonnes of data, these algorithms can scan through all the information available over the years to predict future threats and identify the source and patterns so that preventive measures can be taken in advance. This is beneficial in that it creates links between individual theft cases, allowing analysts to better assess what the best plan of action is in response.

Dataset Scaling

The more data that’s collected, the better machine learning algorithms are trained for a variety of situations. Unlike many other scenarios where lots of data mean more complexity, a wider database allows machine learning algorithms to be scaled and adapted as required. It also allows them to grow more accurate with every addition, make comparisons and identify genuine and fraud transactions in an instant– a true step up from the days of human involvement. However, a caveat– in training stages, it is crucial that analysts be monitoring the process because if the machine goes over an undetected fraud without flagging it, chances are it’ll learn to ignore that type of fraud in the future, opening up a big sinkhole in the system.

The final word

Machine learning is revolutionary in preventing billions of dollars being lost in fraud, theft and data recovery. Firms are increasingly allocating a huge chunk of their budget towards sound ML-based security systems– a testament to just how revolutionary the technology is in identity theft detection.

The Perks of Using Machine Learning for Small Businesses!

Posted on by Imarticus Learning

Machine Learning and Artificial Intelligence have often been associated with top-of-the-rank brands such as Google and Apple. That has led to the perpetuation of an idea that AI just isn’t for everyone… and that’s incorrect.

Artificial Intelligence, specifically Machine Learning, is just as accessible and usable to small businesses as they are to tech and finance titans. When it comes to staying ahead of competitors, the situation is make-or-break– emerging technology is the portal through which smaller companies can gain headway in an already airtight industry, or quickly adapt processes that take months to approve in larger corporations.

As with anything new, the future of artificial intelligence and Machine Learning also presents its own sets of stumbling blocks, some of which may prove to be a detriment for smaller companies with limited budgets and skilled personnel. R&D accounts for a large chunk of the expenditure; training and analysing models takes topline human resources.

However, if firms are willing to take the risk and take the plunge, there are a whole host of perks that will have small businesses emerging victorious:

Making Marketing Campaigns Stronger

Marketing is the be-all and end-all of many brands, especially those that heavily rely on brand image and word of mouth to sell products or services. Machine learning can be put to use in marketing in the following manners:

  • Personalising product recommendations
  • Automating cataloguing of products
  • Optimising content from email subject lines to Facebook ads
  • Researching trends and search terms
  • Revamping keywords and SEO strategies

To achieve the following goals:

  • Innovative products and services
  • Happy customers and lesser returns
  • Intuitive and interactive user experiences
  • Diversified revenue streams
  • Reduced marketing costs and subsequent waste

Driving Sales Numbers

When it comes to sales, insights and analyses of data can be a veritable goldmine– this is where machine learning comes in. A solid ML tool can analyse:

  • customer-product interactions
  • past purchases
  • digital behaviour
  • trending search terms
  • popular products
  • transaction types

Using this, firms can identify what leads are likely to convert and equally pay attention to converting hesitant users into loyal customers.

Upselling and Cross-selling

Upselling means getting the customer to purchase a higher or more upgraded product, while cross-selling means pitching products in the same segment or complementary to the product in their cart. Machine learning can be leveraged to produce personalised recommendations of products and services based on analyses of the existing database. By identifying past purchases or inter-linking products, machine learning tools can upsell or cross-sell appropriately, thereby driving revenue and increasing the number of items sold.

Automating Repetitive Tasks

Small businesses are often faced with having to delegate the most menial tasks to precious employees, leaving the latter overburdened and unable to innovate. Using machine learning to automate repetitive tasks can ensure that routine measures are taken care of at scheduled times and employees are left with time to think strategically and fulfil intended roles. Some tasks that are automatable include:

  • Generating and sending email responses
  • Setting up a sales pipeline
  • Collecting and logging payments
  • Gathering and evaluating client satisfaction

Conclusion

Regardless of the industry, machine learning offers several perks for small businesses to help them grow, expand and generate revenue through different streams. From bookkeeping and manual data entry to voice assistants and exclusive data insights, a machine learning course can put you at the. Forefront of the industrial revolution taking the world by storm today.