The Future of Artificial intelligence in Manufacturing Industries

Industry 4.0”- the world is on the brink of this fourth industrial revolution
4.0 brings forth the technologies such as the Internet of things and Artificial intelligence to make things “smarter” – allowing companies to revolutionize the way they manufacture and ship goods. Artificial intelligence (AI) is just now finding its niche in manufacturing, it is one such area where AI has set foot and intensified the smart manufacturing or the industrial revolution.
The need for change is inevitable. With technologies like the artificial intelligence empowering efficiency of businesses and making advancements across various industries, the tech world is witnessing a significant change. But how AI becomes the future of manufacturing? as the technology matures, costs drop, and manufacturers discover the reasons how AI algorithms can make complex decisions. Facilitating to conquer many internal challenges that has been in the industry, either it can be expertise shortage to complexity in decision making or issues related to integration and overloaded information.
AI in manufacturing plants provides a complete transformation in their proceedings. Let’s have a look on the scenarios that makes AI best suitable for manufacturing.
• Virtual Reality
At the time of critical situations virtual reality serves as the best way allowing remotely located people to connect and work jointly on the tasks that require immediate response. Enable new tools that help to perform testing in the virtual world. Simulation and product-creation can help reduce the manufacturing time drastically.
• Automation
Automation of processes in manufacturing industry provides high level of accuracy and productivity which is beyond human ability. Automating can even work in environments that are otherwise dangerous, tedious or complicated for humans. In future it is expected that robotics will be employed in place with voice and image recognition capabilities to minimize human errors and efforts.
• Intelligence
In the long run, many critical human tasks will be replaced by robots which minimizes the space for errors, also every production stage can be closely monitored with the help of sensors and data can be shared with AI and analytics software. Increased output, defect detection and corrective action is much faster, ultimately the entire production cycle is way more efficient.
• Prediction 
Industrial equipment’s are typically serviced on a fixed schedule, irrespective of actual operating condition, resulting in wasted labor and risk of unexpected and undiagnosed equipment failures. Once integrated with AI sensors and networked with each other, devices can be monitored, analyzed, and modeled for improved performance and service.
• Better products 
AI takes over the manufacturing plant and automates boring and ordinary human tasks, workers will get to focus on complex and innovative tasks. While AI takes care of unskilled labor, humans can focus on driving innovation and routing their business to advanced levels
“The future is flexible” with Artificial intelligence, in tandem with complementary technologies like 3D printing and IoT, will induce modular manufacturing requisites to meet rising consumer needs.
Do you wish to be better prepared for the changes that AI will bring about to your manufacturing industry? Need to get a deeper foundational understanding of what AI & machine learning technologies are and how to use them successfully in your business? Contact our experts for an insightful discussion please visit at https://www.intellectyx.com/blog/the-future-of-artificial-intelligence-in-manufacturing-industries/

AI at the Edge: Transforming Machine Vision into Reality

The buzz about artificial intelligence has been growing strong compared to the past years, now its potential has been unleashed and businesses started to reap the benefits of this intelligent technology. Latest update on this is one of the most difficult challenges in AI: Making the device understand what they see. A vision to the machine.

Vision is a primary sense and one of the main mediums in which we live our lives, now it is for machines. As devices takes integral part in our daily lives, we can notice an increasing number of applications fail without adequate visual capabilities. Machine vision is the trending part of AI that aims to give machines a sight comparable to our own, but why would a machine need to see? How is it possible to give vision to a machine with Machine learning and Artificial intelligence!! See to the below ideology behind this visual realm

To solve complex tasks aggressively with minimal errors the “Machine vision” concept came into existence. AI’s applicability in machine vision relies on the affiliated branches of machine learning and more so, deep learning help machines identify and understand images from the real world. This is carried over by teaching the machine learning algorithms to computers to recognize features of an object. Say for example: The computer learns that if something is round and red, it’s an apple. Then a tomato is introduced, and so on and so forth. The machine (computer) continually understands and modify its model based on new information and assign a predictive value to each model, indicating the degree of confidence that an object is one thing over another
Areas where Machine vision find its importance

• Machine Vision inspection takes a crucial part in achieving 100% quality control in diverse industries, manufacturing industry on the top gets greatly benefited. Machine vision inspection reduces costs and ensures a high level of customer satisfaction. This effort is carried out by Machines and making it understand the continued adoption of technologies like neural networks and specialized machine vision hardware, now by this we are rapidly closing the gap between human and machine vision.
• Face Recognition system with Deep Learning based approach significantly improves the recognition based on traditional computer vision techniques. With this advancement recognition rate improved to over 95% with a false positive rate of 1 in 10,000 also accurately detects spoof attacks based on a person’s picture.
• Several custom algorithms are being used in a medical device for accurately measuring the power of the corrective lens. Medical devices with a significant imaging component have very stringent algorithm requirements when it comes to accuracy and robustness. These algorithms involves object detection, real-time calibration and robust feature extraction
In the future soon, we may start to see robots with visual capabilities going above and beyond our own, enabling them to carry out numerous complex tasks and operate completely autonomously within our society. Stay tuned to our blogs and get updates on recent technologies at your finger tips please visit intellectyx.com

GDPR Compliance- Using Machine Learning to Enhance Privacy

In the current global tech market, many companies have their data scattered across multiple networks, for a lot of business (personal) data processing is a significant activity. “Personal data”- it might be any information relating to an individual’s private, professional, or public life which is used, shared and transferred around the globe instantaneously, making it difficult for people to secure their personal information. Even governments also have a security interest in ensuring the protection of personal data as they can see breaching of highly sensitive personal data. Now it’s the time for such organizations have likely identified the process changes they need to ensure compliance. if not, you better hurry

GDPR is the recent reminder compliance comes with a high level of awareness and a comprehensive data management strategy. GDPR is applicable to all members of the EU and EEA from May 25, 2018, and to all companies handling the consumer data of citizens within the European Union (EU), no matter the size, industry or country of origin of the business. Also keep in mind If a firm infringes on provisions of the GDPR it can be penalized up to €10 million, or 2% of the worldwide annual revenue of the prior financial year for the lower level and in the upper level Up to €20 million, or 4% of the worldwide annual revenue of the prior financial year.

Ever wondered! is there any “solution for sensitive data protection?” Adapting to GDPR compliance? also enhancing security…? Ai with ML serves the right purpose by solving cybersecurity problems using computerized analytical processes adhering to GDPR compliance. As the market for enhanced security and privacy grows.

Here are the key roles of machine learning in enhancing data privacy
• Machine learning-driven solutions will provide services without exposing PII (personally identifiable information) that can lead to misuse a person’s identity, such as the name and the surname, or the IP address in combination with the physical address
• Machine Learning models can effectively analyze huge data sets in real time to detect specific patterns, anomalies, and trends. Analyses every single packet where all the sensitive information and potential, PII is being stored
• Machine Learning works well with GDPR’s, uses algorithms to analyze patterns in data, therefore minimizing the need for human supervision and PII exposure
Here are the examples relates and showcases how Machine learning helps in meeting GDPR compliance
• AI can help understand the GDPR and any update or act stemming subsequent or regulation from it. NLP (Natural Language Processing) solutions can read an interpret regulations and analyze actually what the document is about and what changes to the law affect privacy and who are the stakeholders involved, etc. Furthermore, NPL combined with ML can upgrade the game because the latter one requires machine-executable functions.
• Data breaches and fraud becoming a serious issue, can be controlled by AI-powered security analytics. Advancing quickly and representing an unmissable opportunity to improve cybersecurity. In particular, ML can detect advance threats (detects behavioral changes), and also eliminate a large number of manual tasks.

Machine learning will be the most viable solution to the current issues of security, privacy and end-user protection and more than that people are becoming more aware on the risks that being online brings. So, where is your business in the GDPR compliance game? Our idea is to make businesses and governments more accountable for their data and the way they use it. Get to know more about our services in supporting GDPR compliance Reach us….

Together, we can build a secured machine learning solution for the right to protection of personal data.

Extending Five Factor model to Chatbot Personality

https://www.intellectyx.com/blog/chatbot-personality/

When it comes to human personalities, I relate to the five personality traits theory aka five factor model (FFM). I will not go too detail into this as this blog is more focused on how this model can be extended to defining/designing chatbot personality. The Five factor model defines the taxonomy for personality traitsby using the below five factors as listed below –

• Openness to experience
  – defined by the degree of curiosity, creativity and preference for novelty and variety involved. It is also described as the extent to which a person is imaginative or independent and depicts a personal preference for a variety of activities over a strict routine.

• Conscientiousness
  – defined by the degree of efficient and organized. This is usually reflected by the planned vs spontaneous behaviors. In other words, low conscientiousness means more flexibility and spontaneity but can also appear low reliability.

• Extraversion
  – defined by the degree of outgoing and energetic. This is usually reflected by more positive emotions, assertiveness and sociability.

• Agreeableness
  – defined by the degree of friendly and compassionate.This is usually reflected by the measure of trust, helpful and even the level of submissiveness. Low agreeableness also seen as most often competitive/challenging and as argumentative.

• Neuroticism
  – defined by the degree of sensitiveness, emotional stability and impulse control. Higher neuroticism is viewed as more unstable, and insecure.

Phew…now that we have covered the traits, let’s see how we can relate these traits to chatbot personality or chatbot persona by using the dialog and intent

Factors	Intent or Dialogs that could be used to identify chatbot personality	Example chatbot personality BOTs
Openness to experience	This trait could be defined by how focused the BOT on a particular subject is – is it more generalist or specialist BOT?	I see generalist BOTS like Siri, Alexa are more towards high openness to experience vs. specialty BOTS such as task focused – pizza, weather, news bots etc., If Iam creating a Pizza ordering Chabot, I would want it anyhow to ne more task focused vs. openness to experience.
Conscientiousness	This trait could be defined by the approach of how the conversations are conducted – is it more open ended vs. menu driven? More the planned, the more the conversation is orchestrated via menus. More menu driven bots in my opinion could be characterized as high in conscientiousness. Dialogs such as below would relate to more high conscientiousness bots – “Please pick one of the options listed below.” “I don’t understand, Can you pick one of the below”	E.g. Food ordering chatbots where conversations are very focused and can be conducted via menu driven vs. sales process driven chatbots where its open ended.
Extraversion	The way the information is collected – actively or passively. Some bots tend to collect first the information even before proceeding with the business process. The other aspect is not following a predicted path and more outgoing in the responses and opening up conversations such as ““I see you are from LA…What is the weather like today?”	E.g. Sales bot would be more extravert where it can be quite adamant that it needs the information first before any further information could be sent.
Agreeableness	Use of words that share emotions and showing a state of agreeableness in responses. Dialogs such as “I agree…” “Yes, I understand…” “Sorry that you are facing…”	Customer support BOTS should rank higher on trust, helpfulness and empathize more with the customers without any judgements.
Neuroticism	The stable the platform is. The more stable, the less neurotic. I see there are lots of bots which is not well trained or doesn’t have all flows covered or very rudimentary. All these bots would fall under highly neurotic	More mature bots which are very consumer focused with voice assisted services like Alexa, Siri that has been trained over many consumers would fit the profile of low neurotic.

This is my effort towards encouraging users to have a good design and planning around any bot development before deep diving into development. 80% Planning and 20% development is how it should be.