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AI and Businesses

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Artificial Intelligence is in its early stages of adoption in businesses and it’s important that we understand and harness this current advancement in technology to grow our businesses. AI is a promising field that businesses need to take advantage of.

The term AI is a broad term which basically describes machines and computer programs that possess the ability to make intelligent human like inferences. To understand the possibilities of AI in business, we need to understand first the subsets of AI.

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Machine Learning

It’s a subset of AI that utilizes data to improve computer algorithms and processes automatically. In this case, the main source of the machines intelligence is the data that’s fed into the model. This is why the role of data in any business is very essential and goes a long way to impact an institutions growth and decision making process.

Deep Learning

This is a subset of Machine Learning that utilizes more data and neural networks. Neural Networks mimics the human brain neurons and engages a series of algorithms to draw and make decisions from data.

A key factor that has led to the advancement of deep Learning is the recent availability of large datasets and computers with faster processing speed. This is so because the performance of any deep learning model depends largely on the quality and size of data. The model is trained to analyse and detect meaningful patterns that can be used to make decisions and provide insight. Unlike normal machine learning algorithms whose performance declines after a period even with more data, the treasure in deep learning is its adaptive nature and ability to go deeper and improve with more data over time.

Deep learning has continued to achieve amazing feats and has reduced the error rate in various automated tasks significantly compared to traditional methods. The achievement of deep learning in game play, self-driving cars, Image recognition, natural language processing, medical health diagnosis etc. has opened the door to even more possibilities and it is time for contemporary business to take full advantage of these resources. In businesses, deep learning can be used to draw inference from data to optimize decision making, to understand customer behavior, to optimize production and manufacturing etc.

Business sectors utilising AI

  • Sales and Marketing: AI can play a huge role in sales and marketing and is already being used today. Systems have been built to suggest ads tailored to each customer based on browsing history and activities. AI can also help equip sales personnel with insight to improve sales method. Each customers unique needs can be catered to much easily with the aid of this additional intelligence and insight. IBM partnered with Adobe to create an AI based solution that predicts the effects weather has on sales and customer purchasing habits. This solution combines AI data and the Adobe Experience platform to build a smart weather platform. This smart weather system would help improve the accessibility to useful insight so that sales and marketing teams have vital information to work with. Other companies such as Lenovo have also leveraged the Adobe Analytics Platform. The Adobe analytics cloud is a platform that utilizes real time data of customers to provide useful insight and information on customer preferences. This helps the company know what products to focus on and what needs improvement.
  • Human Resources: In HR, many repetitive tasks can be customized with the help of AI. Although it’s still essential that human expertise is utilized, AI can increase the efficiency and eventually reduce cost for this department of businesses. Smart chat boxes can be used to communicate information about a company effectively, modelled systems can be used to review CV’s and speed up several recruitment processes, prediction systems can use human behavioural patterns for screenings and talent management etc. Mya systems is an example of a recruitment platform owned by the company Stepstones. Several companies like Deloitte, Hays, L’Oréal use this platform for a quick and reliable hiring process. The system uses AI solutions such as natural language processing and data analysis to coordinate the entire recruitment process. HireVue is another company that focuses on improving the hiring process through technology. They have been used by over 600 customers and have hosted more than 6 million interviews. They have a variety of platforms for video interviews, pre-hire assessment, game based assessments and coding assessments.
  • Customer relations & Contact centres: AI is already being used in this part of organisations in companies like Burberry, Amazon and Starbucks to provide intelligent customer service. Data gathered from customers are used to predict customer behaviour, offer best line of action tailored to suit each customer’s needs, point out customer patterns and provide each customer with unique products and solutions. This in turn cuts down costs and increases efficiency significantly. The company 1-800 Flowers partnered with IBM’s AI department to create a concierge service bot capable of communicating with customers in a life like manner and suggesting suitable gifts based on the data available about the customer. This AI system utilizes Natural Language generation and processing to achieve its features.
  • Finance: AI can be used to perform repeated calculations with little to no error, predict financial markets and point out useful trends. It is currently being used by American Express to monitor real time transactions and prevent fraud. It can also be used to prevent money laundering, carry out financial trading and to optimize portfolios. Accenture consulting has confirmed that deep learning and automation would lead to significant growth in the coming years. The fusion of AI and cloud if scaled and applied appropriately, is capable of driving this sustainable growth. An example of AI applied in finance is Kensho. It is a section of the company S&P Global and is a hub that creates solutions for businesses using AI and machine learning. Kensho leverages machine learning models, complex data structures and natural language processing to provide solutions and useful insights to business. They have successfully built tools for leading financial institutions like Goldman Sach, Merrill Lynch, Bank of America etc
  • Manufacturing and Production: Several well-known manufacturing companies use AI already to optimize their production process and to reduce errors significantly. AI is in its development stage in the Automotive industry and is being applied to the various branches of car production. In manufacturing, AI is applied to the design, production and quality control departments. Hyundai has successfully introduced robots into their car manufacturing plants. These robots are capable of assembling car parts, inspecting cars and also charging electric cars. In the development stages, Tesla’s autopilot driver assistance system is gaining traction. This system relies heavily on image processing, video processing, data and machine learning models. They are constantly improving its performance to ensure safety is maintained. Other companies like Boeing use augmented reality to detect problem areas and abnormalities in production. Nissan uses AI to improve design, General Motors uses computer vision to monitor robotic parts and in assembling to get rid of faults and downtime and BMW uses it to improve their quality control process. AI can also be used to predict product demand and reduces loss significantly.
  • Building Construction and Automation: With the aid of IOT (Internet of things) devices, computer vision and sensors, buildings can be made to operate in a smart and efficient environment. The heating and cooling systems can be regulated automatically, lighting and other appliances can be controlled and designed to optimize energy consumption. This helps improve security and conserve power and energy. The company Versatile is revolutionizing the construction industry by leveraging data from construction sites. Their product, the craneview increases automation and efficiency by using field data to provide insight. The fusion of AI, rich data and IOT in this product creates a smart construction site. This product helps improve effective time management, reduce human error and equip managers with useful insights for informed decision making. Built robotics is another company that aims to increase automation using AI, sensors and software systems. Their product Exosystem is a software that enables heavy duty excavators operate autonomously like robots. These excavators are equipped with make GPS systems, cameras, proximity radars etc. Some of their other products include a robotic operations software (Everest), remote robot monitoring (Guardian) and robot operation tools (Field kit).

Scaling and Deployment of AI

In any business, it is important that AI is introduced in a scalable and sustainable manner to maximize potential and reduce risk. Research has shown that companies who have an efficient AI life cycle structure are more likely to increase their profit significantly.  The vital parts discussed below are essential in building a standard deployment process for AI.

Data

Data is the backbone of any functional machine learning model and so it’s important that there is an efficient data management structure in place. High quality data needs to be easily accessible.

Data analysis and visioning are essential skills needed and so the manual processing of data needs to be ditched for a laid out automated data management system. This would increase model quality and help save significant time.

Machine learning datasets needs to be divided into a train and test sets. The model is trained with training data and then tested with fresh realistic data not seen by the trained model. This helps the data scientist determine accuracy and make relevant change and improvements.

Model Development

Building the machine model with processed data is the next step and a vital part of the deployment process. The problem to be solved is analysed and a suitable algorithm is selected. Some ML algorithms include regression, random forest, Convolutional neural networks, recurrent neural networks etc. The most suitable algorithm and the best processing technique needs to be selected to ensure optimum performance. Data engineers and Data scientists need to work as a team in a structured environment to ensure maximum productivity.

To build an efficient model, clean and well-structured data is utilized and fed into the chosen algorithm for training. The algorithm then learns the different features, variations and patterns in the data.

Deployment

At this point, the model needs to be presented in a user friendly manner. The machine learning engineer is responsible for managing the model and teams up with the software engineer to present the model in a deployable and easy to use format.

This step incorporates automation, testing, monitoring and heavy maintenance. The customer or users feedback is very valuable and usually determines subsequent improvements and updates.

Categories Blogs

Harnessing the power of Machine learning in the Agricultural sector

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What is Machine Learning?

Machine Learning is the process of using artificial intelligence and computer algorithms to learn systematically. These algorithms can learn from the data and improve over time as more data is ingested. Machine learning has become more popular in recent times and this can be attributed to better computing technologies and the evolving big data industry. There are three different types of Machine learning.

  • Supervised Learning

This is when the model makes use of mapped or labelled datasets to adjust the weights of the trained model. These weights are adjusted by going over the given data set and the best weight is reached after a number of iterations are run. These iterations are called epochs. This machine learning algorithm uses cross validation for its training process. The goal here is to select the best weights for the model so that when a new dataset is fed into the model, prediction or classification can be carried out accurately.

  • Unsupervised Learning

With this type of learning, the model is made to learn on its own without any labelled data. Instead the model is forced to discover hidden structures and patterns in the dataset. This model is then used to solve clustering or association problems.

  • Reinforcement Learning

In reinforcement learning, the model is forced to learn and make decisions in a complex environment. The model is either applauded and receives rewards when the right decision is made or punished when the wrong decision is made. This way, the model is forced to go in the right direction based on the feedback received after every iteration. This trial and error process is what makes up reinforcement learning.

These different methods describe the way the algorithm learns from provided data. Obviously, data is a crucial component of any machine learning algorithm. The quality of data and size of data can go a long way to determine how efficient the algorithm is. As data increases, the algorithm is able to adapt to newer examples and learn important features. These features learned can now be applied to forecast or predict, carry out classifications etc. with minimal errors. This is what makes machine learning very useful as it can be applied to solve various business problems and can help companies make more informed decisions about the future. It can also be used to create smart and automated systems in industries and various sectors one of which is the agricultural sector.

Machine Learning in Agriculture

Farms and the Agricultural sector in general now benefit greatly from Machine Learning algorithms and Artificial Intelligence as they can be applied in various ways to provide useful insight and to create efficient automated systems. The Agricultural sector can be divided into two major areas.

  • Arable Farming
  • Pastoral Farming

Arable Farming is the growing of crops. These crops have different stages and face different problems from time to time. This means that to achieve maximum quality produce, the crops need to be monitored and studied closely to determine its need at each point in time. In the past, this process was carried out manually but in this new age of Artificial intelligence and machine learning, sensors and drones can be used to collect data periodically from the farm. This data is then used to analyse and monitor the growing process of the crops. Some key properties that can be monitored include the soil quality, water level, presence of weed and so much more. The data accumulated is in turn used to control water valves, release pesticide to certain problem areas, apply fertilisers etc. This in turn helps create a smooth and much needed automatic system on farm lands.

With the advancement in computer vision and image processing, drones can now be used to take pictures which are in turn processed and analysed. The machine learning algorithm is able to interpret these images and this can be used to detect weeds among crops, detect diseased crops, differentiate between different crop species and determine the growth level of crops. All these and more can now be done without much human intervention. The acquired data can be used to increase productivity and farm produce and also help the farmer discover major problem areas and carry out the needed analysis and to make informed and effective business decisions.

Pastoral farming is the rearing of livestock. Just like in Arable farming, Machine learning algorithms also help accurately predict animal weights, egg and milk production etc. This gives the farmer adequate insight to adjust diets and food nutrients for the livestock. In farms today, livestock behavioural pattern can be monitored with the aid of different kinds of sensors and cameras. The movement and feeding patterns of these animals can be interpreted and gathered as useful data to determine the stress level, health and nutritional need of the animals and livestock. Machine Learning is used to bridge the gap between the gathered data and actual useful application. All of this helps to provide useful real time insight to the farmer. These insights can be used to create effective dietary plans. This definitely has been significant in reducing production costs and increasing profit margins in this area of farming.

Organisations currently leveraging Machine learning in Agriculture

  • Blue River Technology

This company combines the field of machine learning, robotics and computer vision to detect potential problem areas on the farm and help the farmers take appropriate mitigating action. They mostly make use of automation and robots powered by AI to protect the growing crops from weeds. The company developed a robot called the See & Spray robot which helps with weed control. The machine learning algorithm and computer vision helps detect weeds early in crops and the AI powered robots help with the spraying of herbicides. This method has helped reduce, herbicide resistance drastically while simultaneously saving about 80 percent of funds spent on herbicides. Blue River technology is very successful but occasionally face challenges such as glitches and faults of machinery and robots.

  • PEAT

This Berlin based company specialises in using Machine Learning to in identifying soil defects and nutrients. They developed a product called Plantix which uses computer vision combined with machine learning to detect defects in the soil. This product uses Computer vision and images for the detection and to also provide possible solutions and tips to combat the detected problem.

  • Trace Genomics

This California based company also focuses on using machine learning to provide soil analysis to farmers. This analysis is carried out with the aim of reducing defective crops, maximising soil potential and improving overall yield.

  • Ibex Automation

A UK based company that leverages the power of machine learning to produce high precision robots for farmers and agricultural processes. The company focuses on developing autonomous Machines for weed detection and spraying.

General challenges faced in this field

One of the major problems faced when adapting Machine learning methods to farming is long adaptation periods. The process of gathering data, processing, analysing and providing useful technology to perform various tasks takes time. The whole process needs to be studied and this may lead to lengthy adaptation periods. Different farms and farming processes have different needs unique to them. Therefore proper technology and infrastructure needs to be put in place.

Conclusion

Presently, there are ideas to develop more efficient field robots that would assist humans to carry out farm operations such as weeding, drilling, animal and plant sensing etc. These robots would revolutionise the type of equipment’s and machines currently used on the farms and would create a purely automated environment. Also, the field of Computer vision is gradually advancing and improving in precision. This would help accommodate various types of farm images which would in turn drastically improve Machine learning processes on farms such as weed detection.

It is clear that adapting Machine learning in farms would help reduce cost of production in huge numbers, minimise loss and go a long way to help farmers achieve better results with minimal efforts. In conclusion, machine learning combined with various other technologies has the ability to revolutionise the agricultural sector.

Categories Newsroom

How Machine learning can transform business

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Machine learning involves computers discovering how they can perform tasks without being explicitly programmed to do so. It involves computers learning from aggregation and intelligent processing of Big Data to generate insights. It is important to understand business problems before deploying Machine Learning. This is because some business problems are so complex that human intelligence can sometimes fail to see the connection between variables. Machine Learning applications can be useful in any industry. Common business applications include predictive maintenance, customer segmentation and fraud detection. Machine learning is a growing trend which will shape the technological and business landscape, as well as define business strategy over the coming years.

Click here to listen to a BBC podcast on this important trend.

 

I-MORAN consultancy

Categories Newsroom

Enterprise Architecture Software Market and vendor analysis

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Enterprise Architecture software enables organisations and senior leadership teams to make critical decisions more quickly. The top vendors of enterprise architecture software deliver similar features on their platform with common features composed of Enterprise Architecture, Business Process Management, Portfolio Management, Business Model & Strategy, Governance, Risk and Compliance, Business Logic and Data Management.

These tool vendors also adopt key business architecture frameworks from industry best practices and leaders, making enterprise architecture and business architecture a seamless integration process engineered by a dynamic architecture platform.

As organisations with system engineering capabilities continue to appreciate the need for enterprise architecture tools to integrate and automate their architecture models, the business environment is leveraging the same platform to assess their operational KPIs and strategic KPIs.

Most enterprise architecture software vendors provide both cloud-based and on-premise solutions, with a flexible pricing offering to small, medium size and large organisations

By ensuring continuous improvement, scalable demand and cost flexibility, and an opportunity to invest in the best strategy, organisations are driven to adapt to the reality and the need to deploy a heavy weight enterprise architecture tool.

The market leaders of Enterprise Architecture software include;

  • Avolution
  • BiZZdesign
  • Sparx Systems
  • FIOS Insight
  • Software AG