The multiverse was coined by American philosopher William James in 1895.
Artificial intelligence (AI) supports the idea of parallel universes beyond our own.
However, more research is needed in order to discover the hidden secrets of the multiverse and know more about the origin of our species.
The multiverse is a theory in which our universe is not the only one, but states that many universes exist parallel to each other. These distinct universes within the multiverse theory are called parallel universes. A variety of different theories lend themselves to a multiverse viewpoint. The term multiverse is a hypothetical collection of potentially diverse observable universes, each of which would comprise everything that is experimentally accessible by a connected community of observers.
Advances in AI have allowed us to make progress in all kinds of disciplines – and these are not limited to applications on this planet.
There are 3 types of AI:
- Artificial Narrow Intelligence (ANI), which has a limited range of capabilities
- Artificial General Intelligence (AGI), which has attributes that are on par with human capabilities
- Artificial Super Intelligence (ASI), which has skills that surpass humans and can make them obsolete
The observable known universe, which is accessible to telescopes, is about 90 billion light-years across. However, this universe would constitute just a small or even infinitesimal subset of the multiverse. The multiverse idea has arisen in many versions, primarily in cosmology, quantum mechanics, and philosophy, and often asserts the actual physical existence of different potential configurations or histories of the known observable universe.
A related idea is that of the baby universe, in which a quantum gravitational process would create a new region of space-time that would bud off and potentially disconnect from its parent universe. This would lead to a “tree” of universes unlikely to interact after their formation. The process has been speculated to occur in the interiors of black holes.
The connected multiverses could arise from processes in quantum gravity, a hypothetical theory that would unite Einstein’s theory of general relativity with quantum mechanics.
Cosmologists have found signs that a second type of dark energy — the ubiquitous but enigmatic substance that is pushing the current Universe’s expansion to accelerate — might have existed in the first 300,000 years after the Big Bang.
Data from the Atacama Cosmology Telescope suggest the existence of two types of dark energy at the very start of the Universe. This is key to understand the concept of the multiverse in our timeline.
Image Source: NASA
Artificial intelligence (AI) is useful in space exploration by processing satellite images as well as discovering other planets and galaxies. It can reduce the time required for initial mission design which otherwise takes many human work hours. AI can also help humans solve problems faster. In this manner, scientists can carry out effective and quicker inspections of our universe.
AI is capable of analysing data received from satellites to detect any problems, predict satellite health performance and present a visualisation for informed decision making. It is also being used to navigate spacecraft, probes and even rovers.
Our desire to further explore the final frontier seems to be growing each day. Perhaps the development of artificial superintelligence in the near future is crucial to discover the secrets of the multiverse.
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.
How to Conduct SQL Performance Tuning
How to Conduct SQL Performance Tuning
The concept of SQL Server performance tuning is simple. To put it briefly, if your organization’s functionality involves a lot of powerful tools to handle a range of data, then this system will be the perfect choice for you. It is important to have a properly functioning system to handle data in your organization. For, in a case where the handling system is inefficient, your organization’s resources will be affected.
Other relevant aftermath includes slow performances and the loss of service. This is where the SQL queries come into play; it has the efficiency to smoothen your organization’s functionality when it involves handling data.
By SQL Server performance tuning, you are making the SQL query system more efficient. You can know the concept in depth by enrolling in SQL certification online. The following segment will explain more about the SQL tuning aspect.
SQL performance tuning: a brief understanding
By now, it would be clear that the SQL query system acts as an efficient data warehouse.
But, why does it require tuning? Imagine a general physical warehouse. A warehouse would consist of a variety of shelves, and a number of other elements/ requirements to hold your products properly. Imagine the same physical warehouse being unguarded, in addition to being unattended.
What happens now? All your products could be stolen and could be damaged to a great extent too. Now, imagine this another scenario. You are maintaining the warehouse properly; regularly maintaining the place and attending to small repairs now and then. This would eventually increase the lifetime of your warehouse.
During the course of regular maintenance, you will also make sure that additional products can be added to your warehouse, for more monetary benefits. Likewise, SQL performance tuning gives you more or less the same metamorphic benefits. The better care you give towards the SQL performance tuning, the better the overall SQL performance be.
To simply put, the SQL server generally has a lot of processes as well as procedures; this is to enable efficient functioning of the system. The performance tuning aspect helps in optimizing maximum efficiency in general. Furthermore, the tuning of SQL ensures that MySQL, as well as SQL servers, get benefitted as well.
Importance of SQL performance tuning
- Faster Retrieval
Be it a physical warehouse or SQL data warehouse, the primary aim to store anything is to retrieve it easily. Imagine if it takes hours to get the simplest item from your warehouse; you would be frustrated of course. To be honest, it would be more annoying to sit in front of your workplace device to see the “loading” message. For, you know that a simple delay can alter your day’s schedule to a great extent. To avoid such inconveniences, it is always important to tune the performance of SQL. You can know more about this by checking out SQL interview questions.
Another key point to note here is that, as per several pieces of research conducted across the globe, the speed of your organization’s systems decides your client’s purchasing mood. Thus, the slowness of your SQL performance can impact your client too.
Overall, it is a required action to improve the data retrieval speed of your organization.
- Avoiding coding loops
To put it in simple terms, the concept of coding loops is a set of repetitive instructions. These repetitive instructions continue to happen till the conditional goal/ destination is reached. For an instance, if you want to modify a particular data, the loop will happen only till the modification of data only.
The loops are effective only when it is properly functional. Improper coding loops have the capacity to damage your data. Thus, by tuning the SQL performance, the loop will happen only one time. Without the fine-tuning of the SQL performance, the loop will happen many times, which would have aftermath on your data in general.
Best practices to follow to effectively tune the performance of SQL
- Analyze the task time
The best way to find the health of your server system is by starting your investigation at the task response level. Give your SQL system a simple task to perform and note the time taken. Simultaneously, give a complex task to your system and again note the time. Now check if there is any unreasonable lag as such.
Currently, there are several third-party apps/ software are available that would consistently check the speed of your system and would immediately notify you if there is any suspicious lag.
It is always recommended to record such results and monitor the improvements/ derailments in general.
- Examination is the key
As a part of an organization, you know that every set of data falls under a particular category/ sub-category. Having said that, since you would have to deal with a large chunk of data in general, it is always good to have relevant filters. If you do not have filters as of now, you should seriously consider implementing the same. This will help in fine-tuning the SQL performance to a great level. You can know more about this by checking out the video below:
Enhancing your SQL is important to keep your data safe and strong. It is also important to improve the overall performance of your database too. By implementing the fine-tuning mechanisms on your database, you are set to have a high-functioning database in your organization.
Neelabh Verma is a content writer at Intellipaat having 5+ industry experience in databas management
Obstacles and Opportunities of Democratizing AI for Organizations
Enterprise deployment of artificial intelligence (AI) is positioned for tremendous growth.
Artificial intelligence is set to change the business world by improving predictive analytics, sales forecasting, customer needs, process automation and security systems.
IBM’s Global AI Adoption Index revealed that a third of those surveyed will be investing in AI skills and solutions over the next 12 months.
More expansive use of AI democratizes AI, providing access to insights to more people – technologists and non-technologists alike. The latter group might include people in leadership, sales, finance, human resources and operations. This is where AI will shine, empowering business teams to make AI-driven decisions.
Imagine: business teams do not have to know how to code or be schooled in the intricacies of AI’s backend. Instead, they will use AI like you and I use a mobile phone for efficiency (if we’re running late, we merely send a text notifying the other person), access information faster (if we’re in the grocery store and need a recipe, we look it up), make better decisions (GPS gives us the fastest route).
Just as mobile technology works without us understanding complex circuitry, algorithms or software, the democratization of AI across enterprises will be integrated in much the same way.
So, what will hold AI back and how will AI help enterprise companies gain traction?
3 Obstacles and Opportunities Organizations Face by Implementing Artificial Intelligence
Artificial intelligence deployment approach | Source: IBM
Obstacle #1: Data in disarray. Data that does not provide a complete picture and single version of truth because of data silos and various data formats within an organization.
Opportunity: Employing a data fabric. Using a data fabric to help organizations use data more effectively and get the right data to users regardless of where it is stored. One significant advantage of a data fabric is that data governance rules may be automatically set for compliance.
Having one information structure to garner insights and analytics from, integrating security to protect sensitive data and establishing a framework for implementing trustworthy AI positions AI as part of the business strategy, not solely an IT strategy so that AI directly impacts business operations.
It all comes back to connecting data with business drivers and a data fabric helps accomplish this. It is what I call “point-to-point” thinking – knowing the business imperatives, business drivers, the different levels of raw data, who is consuming the data, who will have access to the data, and why the data is important in decision-making and then, the big payoff with AI, how it will elevate experiences: customer experience, workforce experience, supply chain experience, strategic partner experience, community experience. In “point-to-point” thinking we don’t hoard data, but share it – securely.
Obstacle #2: Varied skill levels. A lack of AI technical skills across the enterprise and a reliable, open platform to bring AI to more people.
Opportunity: Creating a bridge to AI for people within the enterprise. Palantir for IBM Cloud Pak for Data is one of the great innovations of our time because it doesn’t require coding skills. People in non-technical roles can go from raw data to data insights quickly using application templates (think of all the designs being produced with minimal design experience because of apps like Adobe Photoshop and Canva). This is truly the path to democratizing AI.
People can now use AI to make better decisions in real time and improve business outcomes. These teams include sales and marketing, manufacturing operations, campaign managers, branch managers, franchise operators, human resources, among others.
An example: a customer walks into their regional bank. The banking professional greets the customer, invites them to sit down and pulls up their profile. They see, not only account information, but a 360-degree view of the person sitting across from them. Through a data fabric, non-tabular visualizations gathered from previously siloed data originating from different systems provides an AI-infused perspective.
This might include two algorithmically recommended customer offers inspired by marketing analyst data and intelligent customer segmentation and campaign propensity scoring powered by Watson models.
Going further, feedback from the customer can then be entered and that data influences future offers because it goes right back into IBM’s data and AI platform. IBM Cloud Pak for Data, which helps to simplify data management and protect sensitive data by establishing a framework for implementing trustworthy AI.
Obstacle #3: Solving for the wrong “x.” In hundreds of conversations I’ve had with enterprise leaders over the years about AI, one common failure I see not identifying the right problem or identifying use cases that will yield high return from AI.
Opportunity: Clearly articulating the problem to be solved. With AI, we are talking about a machine making reasonable conclusions based on data. Better defining the problem is akin to asking better questions.
Imagine the difference if you were in a store and asked someone if they sold products. The question is too vague to expect a meaningful answer. Ask where the tomatoes are and you get a clear answer. Both are valid questions, but one is more focused. That’s how defining the problem should be (this is not just for AI purposes; I devote a lot of space in my book, Ascend Your Startup, to defining the customer problem because I believe building the wrong solutions plagues many companies)
In an interview with famed Mount Everest climber George Mallory, a reporter asked him why he wanted to climb the formidable mountain. His answer: “Because it’s there.” AI is very much the same thing. It has obstacles, yet it has the allure of opportunity and of making measurable progress.
Here are the big three takeaways for enterprise companies:
• Use a data fabric. Information is powerful – and it exists! Don’t let siloed data and inconsistent data formats hold people back from making better decisions.
• Give people what they need to succeed in their jobs. Tools such as low code/no code enable business users to rapidly leverage data and apply AI in their decision making.
• Go back to square one and define the problem. Solving for “x” without fully understanding “x” wastes precious time, causes unnecessary frustration and marginalizes the experience for everyone involved.
The Rise of AI
In a Forbes article on the topic of AI, author Manas Agrawal writes, “With rapid learning and adoption, AI is no longer a crystal ball technology but something that humans now interact with in nearly every sphere of life.”
In a very short time, we won’t be talking about AI adoption as people see it as part of doing business and part of making life more efficient. AI then will shift to being part of an enterprise’s business strategy, delivering value for non-technical people working in many different areas like customer experience, brand differentiation, HR, research and development, management and sales.
This is what the democratization of AI looks like at the crossroad of technology and humanity to improve outcomes for people leading successful enterprise businesses.
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