Machine learning or Artificial Intelligence are helping businesses in obtaining actionable insights, achieving key goals, accelerating decision making, and creating innovative products that thrive. Right from your Google Assistant to self-driven cars, these AI and ML industries are growing at a drastic rate and continue to make exciting revelations.
The industry is new and will open a variety of jobs for the youths in the future. However, before going any further, let’s first understand the basics of Artificial Intelligence.
An Insight into ML and AI
In simple terms, Machine learning is a branch of Artificial Intelligence that defines the capability of machines to act or perform human behaviors without being programmed for.
The way by which social media shows you ads, Netflix shows recommended shows, Amazon gives you shopping ideas, your predictive text, and much more, all because of the machine learning algorithms working behind it.
However, there are many challenges faced by Machine learning professionals; let’s have a look at them.
ML Bluetooth Authentication
The connection between two devices occurs mostly with the help of Bluetooth. A group of researchers establishes device-to-device authentication with the help of authentic interaction between two devices that can help in resolving the pairing problems.
Verification of Interaction Authenticity (VIA) solves the problem of authentication and automatic de-authentication. Normally we can connect our Bluetooth devices with other devices freely, which raises privacy concerns, so VIA makes it easier for users by taking features from packet headers and comparing them with the verification model.
Identifying any deviations from the authentic interaction will automatically disconnect the devices from each other.
Complex Process & Slow Programs
Every action in the field of AI and ML requires multiple hit and trial methods, yet the consequences after the final model are still not known fully. That is why this includes a high chance of errors. It includes a variety of data, processing of data, removing data bias, training data, and much more. Thus, this all corresponds to be a very complex process.
Even if you have a good quality of data for accurate results, the problem lies with the slow program. With machine learning, you might get the required result, but slow programs, data load, and many such things take a lot of time to come up with the required result. With that, a constant monitoring system is much required to get the required result.
Involvement of Right Team
Having the right team into action is what these innovative models require right from the beginning. The right team includes not only technical people but also non-technical individuals. Don’t forget about cybersecurity. Include someone like a cybersecurity consulting manager to your team. It’s an additional cost but safety is number one priority!
It is believed that the non-technical team is as important to have as a technical team in machine learning. Because machine learning processes have to be produced in a way that they can react the same way as humans.
For this, we need to have a diverse team of people to include each other’s ideas in a common model. The technical team includes subject matter experts that can look for the training data along with the product managers with the expertise of establishing the business objective in the right direction.
Apart from that, the team involves user researchers that can validate the model’s performance and the ethic teams to identify the sensitive areas. At last, the model has to be useful enough to be converted into a business process or customer experience.
Good Quality Data
In the pilot stage, only a few Artificial Intelligence models go for production; the rest fail to make it to the final stage probably because they don’t work on the right problem. Knowing a problem in machine learning is much more crucial than solving a problem.
Data plays a very important role in terms of machine learning. Many people try to upgrade the model, but the main problem lies with the collection of data. Good quality data can be used in models that will give much more accurate results.
But the public database is not just useful for the working of AI; either you need to gather more accurate data or buy it from any third party. Next, the collection of data is itself a new problem as businesses never know if the data is authentic or not.
In the fields like healthcare and banking where privacy of the customers is the utmost priority. Any kind of data discrepancies or leakages can severely affect the whole system. Therefore, the good and accurate quality of data is much needed in ML.
Training the Data
Training data is the resources you use to train AI and ML algorithms to make them ready to predict the problem that these models are designed for. If you use a hybrid model, the data will be enriched and strengthened with data annotation or labeling.
Simply put, the training is used to train, test, and validate the AI models using machine learning and deep learning algorithms. In the unsupervised learning models, the models use unlabeled data.
