75 billion smart devices connected with each other will be used in our homes and offices by 2025. They will be making decisions on their own without communicating with us or the cloud.
If we want to use these well-connected devices and let them make decisions, we must make sure that they are ethically secure and using the secured AI and machine learning operations for us.
Code of conduct is not just enough to make these devices safe for use. Industries involved, need to make sure that the structure of their systems is the safest and ethical decisions are made. They also require physical intervention if the system fails to obey the ethical code to conduct.
As an Internet of Things (IOT) is growing and artificial Intelligence is becoming the key component of computing. Artificial Intelligence ethics is becoming a key issue to be addressed. Stats show that over 750 million AI chips are sold in 2020. Their processing power is increasing and they are now part of smartphones, security cameras, thermostats, and many other smart devices. These systems are getting smatter through machine learning and their dependency on the internet for decision making is reducing.
Investing in reliable and safe AI/ML systems depends on the design and development and collaboration with humans in a comprehensive way. It is very important to implement privacy and security at the beginning of the system development. They can not be implemented at the later stage of the system development.
These systems require the highest level of security implemented on every level of the development phase in both software and hardware level systems must be capable of processing input data. It is noticed that advanced cryptography solutions are being used in these days.
Hardware security will play an important role to prevent AI/ML based system attacks to exploit sensitive data from secure systems. Devices with sophisticated data must be equipped with security measures to counter such attacks.
The accountability of these systems right now is not consistent. AI ecosystem is a contribution of different creators. So, to make these creators accountable is not yet possible until all the creators are on one platform and they make a comprehensive code to conduct for the AL/ML systems.
A tiny vulnerability can collapse the whole AI ecosystem.
Are you an ECommerce stakeholder? You must be looking for ways to improve conversion, reduce cost and time. This piece would cover how Artificial Intelligence(AI) can help you achieve these goals.
According to recent estimates, E-Commerce sales are set to top $27 trillion by the year 2020.
Here are the functional areas of (A.I) Ways can help you
1. User Experience by AI:
Our first priority is Shoppers have a high bar on user experience. Machine Intelligence(MI) can influence them with a best-in-class user experience. It helps you in designing a brand new website or adding smart widgets to your existing site. UX and 1:1 personalization has become a significant factor for success, it would maximize your ROI. Landing a user on a page without a good experience would eat the marketing dollars with a low conversion.
2. Predictive pricing and incentives:
Artificial intelligence (AI) tools can add a dynamic pricing layer to your store. They use Machine learning and Data science to understand your user behavior and change the product price in real-time. Some advanced tools consider competitor pricing and market demand. Of course, they change within the parameters specified by you.
3. Marketing & Analytics:
Today many subsections of marketing use machine learning like user data enrichment, user identification across devices, intelligent Ads personalization, predictive analytics and so on.
4. Visual search:
Your shoppers can search for items in your catalog just by taking a picture on their phones. AI picks similar products based on color, texture, shape and so on. These tools would help fashion stores a lot and makes sense for a store with a large catalog.
5. Growth in neural networks and human-like technologies
We have been using traditional Machine Learning (ML) algorithms for quite long, and we couldn’t move forward. In the past few years, we have had a breakthrough in neural networks. Deep learning is a new method to achieve closer to human results. In specific areas like computer vision, it has bypassed human efficiency. We are in the era of productization of all these AI technologies. Now, most A.I tools use a combination of traditional ML methods and deep learning together to achieve their goals.
We have covered a selected few functions of E-commerce in this post. The primary goal of this article is to highlight the trending use cases of A.I in ECommerce apart from chatbots and personal assistants. I’d love to hear your thoughts on the same.