Businesses that realize the value of data and contemplate machine learning implementation are usually faced with similar challenges, that can be overcome by following a few simple guidelines.
Business leaders are increasingly becoming aware of data science and machine learning’s role in supporting and enhancing business growth. In an attempt to leverage the technology, businesses either jump into implementation without planning or are stuck in the planning phase for too long, both leading to suboptimal outcomes. As business and technology leaders involved in machine learning implementation, you should keep the following tips in mind to ensure that you are doing it the right way:
1. Use the Right Data
Like any other analytical or logical application, the principle of Garbage in, Garbage Out holds true for machine learning. When it comes to machine learning implementation, although the algorithm is considered to be an important factor for driving success, the data that is fed to it is equally important, if not more. The quality and relevance of data used in machine learning helps extract highly valuable insights and sets the machine learning initiative in the right direction. Choosing the right variables to track and process through the algorithm requires asking the right questions and verifying the data quality.
2. Experiment with Algorithms
Successful machine learning implementation requires a combination of quality data and a robust algorithm. It may not be easy to get everything right with your algorithms in the first attempt, requiring you to make improvements based on trial and error. Investigating your algorithms to pick out behaviors that are desirable, as well as, the ones that are not, will enable you to modify algorithm parameters to achieve the ideal results. You should also realize that algorithms that track highly complex data, such as that associated with humans, need constant re-evaluation and re-programming to ensure sustained effectiveness.
3. Collaborate with Research and Academic Institutions
One way to make up for lack of expertise in machine learning implementation is partnering with academic institutions that research and teach machine learning and analytics. This will enable organizations to make use of subsidized expertise while making progress using machine learning. An example of such collaboration between business and academia is the Prototype optimization model made by Wayne State University that saved $12 million for Ford Motor Company on the first use. As a long-term initiative, businesses should invest in data science labs, to promote such fruitful collaborations with academic institutions.
4. Train Employees in Machine Learning Implementation
Another way to compensate for inadequacy in know-how is to ‘upskill’ existing employees through training to make them proficient in data science applications. Employees for an upskill should be selected based on aptitude for work in question. The most suitable candidates to receive such a training are the ones with a high aptitude for math and statistics, and the ability to translate data into useful insights. Organising training seminars and workshops are among the most common ways to train employees in a chosen skill.
5. Hire Third-Party Expertise
As there is high demand for data science experts, businesses can consult third-party specialists to help them initiate and integrate machine learning into the business. Experienced third-party professionals can guide your business through planning and execution of pilot projects, and educate organizational personnel on data science and machine learning.
Keeping these tips in mind will enable you, as a CIO or technology leader, to keep your machine learning implementation on the right track. To make the most of any technology, the best way is to gain enough practical knowledge about it. In addition to the tips mentioned above, you should also acquaint yourself with some machine learning best practices.
How Big Data and Artificial Intelligence Can Create New Possibilities
By combining artificial intelligence (AI) and big data, organizations can see and predict upcoming trends in key sectors including business, technology, finance and healthcare.
AI is the simulation of human intelligence by computers. By applying machine learning algorithms, we can make ‘intelligent’ machines, which can employ cognitive reasoning to make decisions based on the data fed to them. Big Data, on the other hand, is a blanket term for computational strategies and techniques applied to large sets of data to mine information from them. Big data technology includes capturing and storing the data, and then analyzing data to make strategic decisions and improve business outcomes. Most companies deploy big data and AI in silos to structure their existing data sets and to develop machines which can think for themselves. But, big data is in reality the raw material for AI. So, when big data meets AI, they have the potential to transform both, the way data is structured and the way machines learn.
What is Artificial Intelligence and its Subets?
Artificial intelligence (AI) leverages computers and machines to mimic the problem-solving and decision-making capabilities of the human mind.
It is a constellation of many different technologies working together to enable machines to sense, comprehend, act, and learn with human-like levels of intelligence.
Here are the subsets of artificial intelligence:
- Machine Learning.
- Deep Learning.
- Natural Language processing.
- Expert System.
- Machine Vision.
- Speech Recognition.
What is Big Data and Its 3Vs?
Big data is data that contains greater variety, arriving in increasing volumes and with more velocity.
Although the concept of big data itself is relatively new, the origins of large data sets go back to the 1960s and ‘70s when the world of data was just getting started with the first data centers and the development of the relational database.
Benefits of Big Data and Artificial Intelligence in the Digital Era
Corporations analyze and manage extensive data sets every day. Client information, employee details, business statistics, all put together, can be a huge collection of unstructured data that can be sorted and studied for business optimization. Big data provides solutions to collect and store data in a robust manner, while AI, with its machine learning techniques, learns from the data sets to make better decisions in the future.
Here are the benefits of big data:
Big data cuts business costs.
Big data increases efficiency.
Big data improves pricing.
Big data provides more tools to compete with big businesses.
Big data allows organizations to focus on local preferences.
Big data helps increase sales and loyalty.
Big data ensures you hire the right employees.
The retail brand Walmart is already using big data with AI to revise their business structure. With over millions of customers accessing their online and offline stores every single day, Walmart collects customer data in the range of petabytes. Big data analysts work on the vast data set, helping their machine learning algorithms master the decision-making skills. Studying the trending products on the site, patterns in customer buying habits, and relations between the demand and supply of goods, helped Walmart remodel its website and inventory to suit the needs of their customers, thus boosting their business.
AI algorithms usually work on sample data sets to in the machine’s initial stages of learning. However, clubbing the algorithms with live data allows machines to learn from actual data sets rather than sample ones. Thus, we can efficiently train our machines to make better decisions right from the learning stage.
An excellent example of this comes from the meteorology department. Servers in weather observatories receive data in the form of text, images, and videos from satellites, weather stations, and relay boards from all over the world. Big data coupled with AI is used in these domains to efficiently store the data and then work on it using image and video processing techniques for weather predictions.
Here are the benefits of artificial intelligence:
1) Less Human Errors: There is less room for error with artificial intelligence.
2) Doing More Complex Tasks: Artificial intelligence can perform a more laborious task with extra hard work and with greater responsibility.
3) Available 24/7: Educational Institutes and Helpline centers are getting many queries and issues which can be handled effectively using AI.
4) Providing Digital Assistance: Virtual assistants inside smartphones, PCs, or connected home speakers, like Apple’s Siri, Microsoft’s Cortana, Google’s Google Now, Samsung’s Galaxy S8’s Bixby and Amazon’s Alexa, provide contextual information.
5) Assisting Humans in Repetitive Tasks: In banks, we often see many verifications of documents to get a loan which is a repetitive task for the owner of the bank. Using AI Cognitive Automation the owner can speed up the process of verifying the documents by which both the customers and the owner will be benefited.
6) More Productive: Emotions are not associated with artificial intelligence robots and therefore the mood doesn’t hamper the efficiency. Thus they are always productive.
7) Educating The Next Generation: Nowadays, medical professionals are trained with artificial surgery simulators. It uses applications which help in detecting and monitoring neurological disorders and stimulate the brain functions.
8) Right Decision Making: The integration of AI tools in the business world has improved the efficiency of organizations.
Although computers cannot match human brains on a cognitive level, they are essential to sort and organize the vast data sets we deal with in the modern world. By merging AI and big data, we can obtain a structured real-time database, which can further be used in a variety of applications. Though the merger of these two domains is still in progress, we can expect rapid breakthroughs in the way we handle extensive data sets in businesses and in everyday lives.
How Virtual & Augmented Reality are Revolutionizing the Mining Industry
Virtual reality (VR) and augmented reality (AR) are technologies that are quickly becoming great tools for many types of industries.
From tourism to manufacturing and everything in between, both Augmented and Virtual reality are truly changing the way that things work. One industry where VR and AR is relevant and is offering a significant change to the way things are done is the mining industry. More and more mining companies are turning to these technologies as a way to improve safety, efficiency, and to drive innovation.
Over the past several years the mining industry has invested about 0.5 percent of their revenues into R&D. The investment into new innovations such as augmented reality and virtual reality technologies will help to insulate the mining industry from volatility and will also help to ensure future profitability.
There are several ways that both virtual and augmented reality can be useful in the mining industry. Some of the ways that VR and AR can be utilized include for equipment training, safety training, field solutions, and to provide site tours of the mines.
Let’s take a closer look at what VR and AR are as well as the different ways that both of these types of technologies are already being used and may be used in the future for the mining industry.
What is the Difference Between Virtual and Augmented Reality?
When it comes to understanding how virtual reality and augmented reality can help revolutionize the mining industry, the first step is to understand exactly how each of these technologies work. Many people do not understand the difference between these two technologies.
Virtual reality is a computer generated stimulation. It is artificial and recreates a real life situation or environment. Users are immersed in the virtual world. They feel like they are enveloped in reality firsthand. This is primarily through hearing and vision.
Typically, virtual reality will be delivered through a headset that is equipped with the technology. Most often, virtual reality is used to enhance reality for entertainment or gaming. It is also used to enhance training in a real life environment by simulating a reality for people to practice beforehand. One prime example of this is a flight simulator used by pilots. In the mining industry it can be used to create practice scenarios of where dynamite can be placed in order to create the appropriate cracks and to avoid avalanches and other dangerous situations.
Augmented reality uses layers of computer generated enhancements on top of existing reality. This makes it more meaningful when interacting with it. Augmented reality is developed as apps and then used on a mobile device to blend a digital component with the real world. This is completed in a way that enhances both realities, but it can also be easily told apart.
AR is becoming more and more mainstream as it is used in 3D photos, emails, and texts for mobile devices, and for overlays for televised sports. The tech industry uses motion activated commands and holograms as well as other revolutionary things.
Differences between AR and VR
The main difference between AR and VR is that VR recreates a real life setting digitally, while AR uses virtual elements as overlays for the real world.
Why Use Virtual Reality in Mines?
Virtual reality and augmented reality can greatly change the landscape of how training is conducted, especially in jobs that are dangerous, such as mining. Using controlled explosives in mines is extremely important. However, this can be quite dangerous and even deadly if they are not handled correctly.
If the explosives are not placed in the correct locations and accurate measurements are not made, the rocks may fracture in ways that are not expected. This can create a hazardous situation, not only underground, but above the surface as well.
Virtual reality provides a way to practice the art of placing explosives without any of the danger by using a simulated VR mine.
How is Virtual Reality Being Used in the Mining Industry So Far?
Simulated Training Solutions, which is a South African company, created the first VR blast wall in 2016. This VR blast wall was installed in Zambia at the Mopani Copper Mines. Since this first wall was installed, there have been two others that have been installed in locations in South Africa and there is a third that is being created.
This training device allows trainees to practice their skills. While sitting in a dark training room, users will see a rock projected onto a large interactive canvas. An electronic spray can will be used to mark the measured blast holes. Once the blast holes have been marked, the trainee will then be able to practice detonating the explosives in the correct sequence. As they are doing this they can watch to see how the rocks react and fracture. Any mistakes that are made are highlighted through the markings.
These virtual walls are a very close representation of what miners will be dealing with when working in the real world. This means that the virtual technology is providing extremely effective job training for miners. In the past blackboard exercises and videos were relied on for training and these methods are simply not as effective as the virtual wall.
When the virtual technology is used to train blasters, they come out of the training more prepared. When the blasters are well-trained, it translates into savings for mine operators as any blast that goes wrong can result in labour costs, clean up costs, and delayed production.
Technology and Mine Operation
Virtual and augmented reality are not the only new technologies that are being used throughout the mining industry. There are many other technologies that are helping to create a safer work environment for miners.
Some innovations that have been implemented throughout the mining industry include the use of autonomous trucks and drills as well as the use of drones. Drones can be used for a number of applications including equipment inspection, evaluating terrain, and filming blasts. Typically, a drone will provide safe and quick access to different aspects of the mining operation that are in places that are high up or otherwise difficult to reach.
Autonomous trucks and drills have greatly improved productivity as well as on site safety. This type of equipment allows a single operator to be in charge of several drills at once. An autonomous system will also create drilling data that can be used for achieving a better blast and to estimate the ore seam size. The data that is gathered can improve decision making in real time, which is a big benefit of this technology.
Perhaps the most important thing that these types of autonomous drills can do is to remove people from the line of fire. This reduces a person’s exposure to risks and hazards that are associated with operating this type of large equipment. It also reduced injuries related to fatigue.
Addressing Environmental and Social Issues of Mining
In addition to addressing the safety and productivity in the mining industry, new technology is also offering a way to address some of the environmental and social issues that the mining industry faces.
Cobalt is a metal that is mined and used for the batteries found in some cars and in most smartphones. More than half of the cobalt in the world comes from the Dominican Republic and many of the people who work in these cobalt mines are children. In fact, it is estimated that there are over 40,000 children working in the mining industry.
With these new technologies that are coming into place, the hope is that using virtual reality for training as well as autonomous trucks and drills, can help to get some of these children out of the mines.
Creating an Intelligent Mine: Mine Design & Planning
The goal of some companies is to create what is being referred to as an intelligent mine in the near future. There are several solutions currently available that can make this goal a reality in a short amount of time simply by implementing just a few small changes.
Mine Life VR is a product created by LlamaZoo that provides a full spectrum of data sets and design iterations for mine planning. It allows resource management and allocation of resources so mining companies can optimize their offerings. It also allows investors and community relations by giving users a chance to view a virtual tour of the mine site in real time 3D which before has never been provided.
Working in the mining industry has always been quite a dangerous job. Going down into the mines with the risk of cave-ins and other issues combined with the use of explosives on site, have made this job one that is extremely risky. In addition, children being used to do these types of jobs is deemed unethical by many.
For this reason, knowing that there is emerging technology that can help create a safer environment for the people who work in the mining industry is a good thing. Through the use of virtual and augmented reality, a safer work environment can be created by training miners thoroughly and providing an environment that is not only safer to work in, but also more productive.
State of AI and Ethical Issues
How to Regulate Artificial Intelligence the Right Way: State of AI and Ethical Issues
The current artificial intelligence (AI) systems are regulated by other existing regulations such as data protection, consumer protection and market competition laws.
It is critical for governments, leaders, and decision makers to develop a firm understanding of the fundamental differences between artificial intelligence, machine learning, and deep learning.
Artificial intelligence (AI) applies to computing systems designed to perform tasks usually reserved for human intelligence using logic, if-then rules, and decision trees. AI recognizes patterns from vast amounts of quality data providing insights, predicting outcomes, and making complex decisions.
Machine learning (ML) is a subset of AI that utilises advanced statistical techniques to enable computing systems to improve at tasks with experience over time. Chatbots like Amazon’s Alexa and Apple’s Siri improve every year thanks to constant use by consumers coupled with the machine learning that takes place in the background.
Deep learning (DL) is a subset of machine learning that uses advanced algorithms to enable an AI system to train itself to perform tasks by exposing multilayered neural networks to vast amounts of data. It then uses what it learns to recognize new patterns contained in the data. Learning can be human-supervised learning, unsupervised learning, and/or reinforcement learning, like Google used with DeepMind to learn how to beat humans at the game Go.
State of Artificial Intelligence in the Pandemic Era
Artificial intelligence (AI) is stepping up in more concrete ways in blockchain, education, internet of things, quantum computing, arm race and vaccine development.
During the Covid-19 pandemic, we have seen AI become increasingly pivotal to breakthroughs in everything from drug discovery to mission critical infrastructure like electricity grids.
AI-first approaches have taken biology by storm with faster simulations of humans’ cellular machinery (proteins and RNA). This has the potential to transform drug discovery and healthcare.
Transformers have emerged as a general purpose architecture for machine learning, beating the state of the art in many domains including natural language planning (NLP), computer vision, and even protein structure prediction.
AI is now an actual arms race rather than a figurative one.
Organizations must learn from the mistakes made with the internet, and prepare for a safer AI.
Artificial intelligence deals with the area of developing computing systems which are capable of performing tasks that humans are very good at, for example recognising objects, recognising and making sense of speech, and decision making in a constrained environment.
There are 3 stages of artificial intelligence:
1. Artificial Narrow Intelligence (ANI), which has a limited range of capabilities. As an example: AlphaGo, IBM’s Watson, virtual assistants like Siri, disease mapping and prediction tools, self-driving cars, machine learning models like recommendation systems and deep learning translation.
2. Artificial General Intelligence (AGI), which has attributes that are on par with human capabilities. This level hasn’t been achieved yet.
3. Artificial Super Intelligence (ASI), which has skills that surpass humans and can make them obsolete. This level hasn’t been achieved yet.
Why Governments Need to Regulate Artificial Intelligence?
We need to regulate artificial intelligence for two reasons.
First, because governments and companies use AI to make decisions that can have a significant impact on our lives. For example, algorithms that calculate school performance can have a devastating effect.
Second, because whenever someone takes a decision that affects us, they have to be accountable to us. Human rights law sets out minimum standards of treatment that everyone can expect. It gives everyone the right to a remedy where those standards are not met, and you suffer harm.
Is There An International Artificial Intelligence Law?
As of today, there is no international artificial intelligence law nor specific legislation designed to regulate its use. However, progress has been made as bills have been passed to regulate certain specific AI systems and frameworks.
Artificial intelligence has changed rapidly over the last few decades. It has made our lives so much easier and saves us valuable time to complete other tasks.
AI must be regulated to protect the positive progress of the technology. Legislators across the globe have to this day failed to design laws that specifically regulate the use of artificial intelligence. This allows profit-oriented companies to develop systems that may cause harm to individuals and to the broader society.
National and International Artificial Intelligence Regulations
National and local governments have started adopting strategies and working on new laws for a number of years, but no legislation has been passed yet.
China for example has developed in 2017 a strategy to become the world’s leader in AI in 2030. In the US, the White House issued ten principles for the regulation of AI. They include the promotion of “reliable, robust and trustworthy AI applications”, public participation and scientific integrity. International bodies that give advice to governments, such as the OECD or the World Economic Forum, have developed ethical guidelines.
The Council of Europe created a Committee dedicated to help develop a legal framework on AI. The most ambitious proposal yet comes from the EU. On 21 April 2021, the EU Commission put forward a proposal for a new AI Act.
Ethical Concerns of Artificial Intelligence
Police forces across the EU deploy facial recognition technologies and predictive policing systems. These systems are inevitably biased and thus perpetuate discrimination and inequality.
Crime prediction and recidivism risk are a second AI application fraught with legal problems. A ProPublica investigation into an algorithm-based criminal risk assessment tool found the formula more likely to flag black defendants as future criminals, labelling them at twice the rate as white defendants, and white defendants were mislabeled as low-risk more often than black defendants. We need to think about the way we are mass producing decisions and processing people, particularly low income and low-status individuals, through automation and their consequences for society.
How to Regulate Artificial Intelligence the Right Way
An effective, rights-protecting AI regulation must, at a minimum, contain the following safeguards. First, artificial intelligence regulation must prohibit use cases, which violate fundamental rights, such as biometric mass surveillance or predictive policing systems. The prohibition should not contain exceptions that allow corporations or public authorities to use them “under certain conditions”.
Second, there must be clear rules setting out exactly what organizations have to make public about their products and services. Companies must provide a detailed description of the AI system itself. This includes information on the data it uses, the development process, the systems’ purpose and where and by whom it is used. It is also key that individuals exposed to AI are informed about it, for example in the case of hiring algorithms. Systems that can have a significant impact on people’s lives should face extra scrutiny and feature in a publicly accessible database. This would make it easier for researchers and journalists to make sure companies and governments are protecting our freedoms properly.
Third, individuals and organisations protecting consumers need to be able to hold governments and corporations responsible when there are problems. Existing rules on accountability must be adapted to recognise that decisions are made by an algorithm and not by the user. This could mean putting the company that developed the algorithm under an obligation to check the data with which algorithms are trained and the decisions algorithms make so they can correct problems.
Fourth, new regulations must make sure that there is a regulator that can make companies and the authorities accountable and that they are following the rules properly. This watchdog should be independent and have the resources and powers it needs to do its job.
Finally, AI regulation should also contain safeguards to protect the most vulnerable. It should set up a system that allows people who have been harmed by AI systems to make a complaint and get compensation. Workers should have the right to take action against invasive AI systems used by their employer without fear of retaliation.
A trustworthy artificial intelligence should respect all applicable laws and regulations, as well as a series of requirements; specific assessment lists aim to help verify the application of each of the key requirements:
Human agency and oversight: AI systems should enable equitable societies by supporting human agency and fundamental rights, and not decrease, limit or misguide human autonomy.
Robustness and safety: Trustworthy AI requires algorithms to be secure, reliable and robust enough to deal with errors or inconsistencies during all life cycle phases of AI systems.
Privacy and data governance: Citizens should have full control over their own data, while data concerning them will not be used to harm or discriminate against them.
Transparency: The traceability of AI systems should be ensured.
Diversity, non-discrimination and fairness: AI systems should consider the whole range of human abilities, skills and requirements, and ensure accessibility.
Societal and environmental well-being: AI systems should be used to enhance positive social change and enhance sustainability and ecological responsibility.
Accountability: Mechanisms should be put in place to ensure responsibility and accountability for AI systems and their outcomes.
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