Showing posts with label Artificial Intelligence. Show all posts
Showing posts with label Artificial Intelligence. Show all posts

Sunday, August 23, 2020

The brain-computer interface

The brain-computer interface, through which the brain will now directly control the electronic device

It may seem strange to you that in the future you will be able to control any device including mobile, computer, laptop directly from your mind without touching it. Of course, you may have seen Dr. Octopus in the movie. In the film, there are many hands of Dr. Octos's machine.

Dr. Octopus controls the hands of these machines with his brain. You may not believe that all this is possible even in real life.

But it is really possible. Brain-computer interface technology now makes it possible to control electronic devices.

After all, what is this brain-computer interface?

Today we are going to tell you what is this brain-computer interface and how it works. You will also find information on its usefulness.

The brain-computer interface is easy to understand if it is a kind of machine or a kind of system. Which connects our brain to the computer and sends the brain's instructions to the computer.

We can even recognize it by another name NCI. Whose full name is Neural Control Interface? To make it easier to understand, such a machine or system, through which the information of our brain can be displayed on the screen of a computer or electronic device. In fact, the system allows you to communicate or give instructions directly to the commuter.

Knowing this, you must have found it interesting and unique. Now let's talk about how it works. To understand the brain interface commuter interface, let's take an example from real life.

Most people know how our brains work. If you know that, you can easily understand this brain-computer. In particular, our brain uses electric pulses to exchange information and instructions with different parts of the body.

This is what we can think with the help of electric pulse and signal. Can work We can dream In this way our brain can do different kinds of things.

Similarly, if you have a general knowledge of computers, the computer also works on the basis of electric signals. From which the computer works based on the instructions we give you.

The electrical signals that work on a computer are zero and one digit in binary form. Now the thing to understand here is that the commuter also works from the electric signal and the brain also uses a kind of electric signal.

Now, in this case, we can make a connection between the two. This means that if we can transmit the kind of electrical signal emanating from our brain to a computer through a system or machine, then the computer can understand the language of our brain.

How much fun would it be to have this very interesting topic in real life? Scientists have completed various tests. The first test was performed on a monkey.

In which some pins were attached to the monkey's head and it was connected with a wire. In this test, the monkey was found to be able to control the robotic arm.

This is exactly what you are looking for in a Doctor Octopus movie. He can move the hands as he pleases without speaking the hands of the machine attached to it.

Now the brain-computer interface technology has become not just a fictional story of the film, but a real-life experiment. In the days to come, this technology will definitely be developed to a more advanced level.

Saturday, June 13, 2020

Use of AI (Artificial intelligence) in Nepal

Artificial intelligence

According to researchers, AI does just what it is designed to do. Scientist Khanal says, ‘Ordinary people cannot recognize the whereabouts of the head and hands of a child in the womb during the ultrasound. If there is software for that, it can be recognized. When trying to post a photo on Facebook, Facebook tries to identify the person and tag them.

How will Artificial Intelligence (AI) affect human creation once it begins its creative work? This question has been raised since the advent of AI as the latest development in science. The question was raised in public when China's state-run Xinhua news agency unveiled an AI news presenter in November 2018.

In 2016, Hanson Robotics, a Hong Kong-based company, built the robot, Sophia. He was even granted citizenship by Saudi Arabia in October 2017. Two years ago, Sofia was brought to Nepal for the UNDP's 'Technology for Public Service' conference, with which many Nepalis interacted. "As Nepal has made good progress in the field of technology, if it is adopted in the right way in the coming days, it can make a big leap forward in development," said Sofia. This sparked the debate on AI in Nepal.

The AI ​​Debate also got a place in the Nepal Literary Festival held in Pokhara in the last week of last December. The debate, entitled ‘Artificial Intelligence and the New Definition of Art’, raised concerns about whether humans would be driven by AI, but researcher Dr. Vinod Bhattarai tried to convince people that the use of AI will make them comfortable.

Before that, the first international AI conference was held in Nepal in October. Research and experience on AI and its various dimensions were presented at the conference organized by British College. The AI ​​conference is being organized by the government next July. Organizing committee member and scientist Suresh Manandhar informed us that the purpose of the conference is to raise investment by giving information about its possibilities and challenges. According to him, international level researchers will participate in the conference.


Use of AI in Nepal

Of course, AI is a new topic for Nepal. There has not been enough research on this. The Nepal Applied Mathematics and Information Institute for Research (NAMI) is conducting research in Nepal. Similarly, an organization called Fuse Machine is active in producing and researching AI engineers. The organization, led by researcher and scientist Sameer Maskey, is also active in creating various apps.

Researchers estimate that AI can be effective in Nepal if used properly. As Nepal is multilingual, the use of AI in language translation can be effective. Khanal has a special opinion. He says AI can also be used to study the effects of climate change and human migration. Another area he sees potential in agriculture and health. "At a time when there is a shortage of manpower in agriculture, small robots can be built and used for mining and other purposes," he said.

What is AI?

The founder of Artificial Intelligence (AI) is American computer scientist John McCarthy (1927–2011). He was accompanied by Alan Turing, Marvin Minsky, and Alan Newell Herbert A. Simon. McCarthy coined the term "artificial intelligence" in 1955.

It includes software that can perform intellectual and emotional tasks just like humans. AI includes software that smells, touches, listens, sees, and imagines. Google's language translation software is one of them.

According to researchers, AI does just what it is designed to do. Scientist Khanal says, ‘Ordinary people cannot recognize the whereabouts of the head and hands of a child in the womb during an ultrasound. If there is software for that, it can be recognized. When trying to post a photo on Facebook, Facebook tries to identify the person and tag them. That kind of identification is done by software. But the software does just that. '

Development of AI

From the 1950s onwards, scientists began researching and using machines that could speak, hear, think, and imagine like humans.

In the 1990s, AI was used in chess, where human intelligence was used extensively. Then I started thinking about the unlimited dimensions of AI. However, the development of AI accelerated only in the decade of 2010. Deep learning and machine learning also became ubiquitous in the 2010s. Just as a small child learns things when he or she grows up, the machine also has a human-learning AI phase, called 'machine learning'. Deep learning is used to teach something in depth.

Autonomous driving is the latest achievement of AI. It includes the concept of driverless driving. Similarly, chat but AI is also being used. This AI is considered to be effective in informing the service recipients about their services.

Along with AI, researchers have started thinking about Artificial General Intelligence (AGI) and Super Intelligence. Under this, a machine that can think beyond the human mind is being conceived.

Challenges and possibilities in AI

Surveys have made many feel insecure and challenged by the development of have been made public. The opinion that employment will collapse after all the work is done through AI has also come together. However, the bright side of AI is very visible. Researchers say that this will help in the rapid development of sectors such as trade, education, and transportation. Scientist Khanal says, ‘There are many companies that have achieved success by combining AI with entrepreneurship. In the context of Nepal itself, by using it, Nepal can participate in the fourth industrial revolution and accelerate the pace of development.


Easy to do research in any genre

To find a new medicine

To promote further scientific inventions

Cheap and quality service in places where there is no expert or expensive technology

To make education effective

To manage traffic in the context of Nepal's urban areas

Creating new jobs and opportunities using AI


Fear of losing employment of the working class

Possibility of misuse by the bourgeoisie

Impact on original culture and beliefs

Low use of labor

Challenges in the creation of creators

Risk of a man being operated by machine, not a machine by man

Wednesday, June 3, 2020

Artificial Intelligence - The Future Of Communication

The future of work is no longer merely a concept, but a reality — Covid-19 has made sure of that.
What role, then, does artificial intelligence (AI) have to play in this drastic shift?
For some time now, I’ve firmly maintained the belief that AI would take over the vast majority of process-driven work within 15 years. However, with years of key developments in the world of work having recently been crammed into a matter of months, the future has unfolded very differently than we imagined.
Rather than coming about through careful planning, companies have been thrust into this new way of working.
Without doubt, many were unprepared for it and have had to move quickly to put in place remote working solutions to keep business going.
They simply didn’t have the time to manage change and implement AI-driven solutions. However, many predict that we’ll see remote working becoming part of the ‘new normal’ even after lockdown measures are eased.

Companies like Twitter, for example, have already announced that their employees can work from home indefinitely.
Assuming that the growing trend towards permanent remote working continues, organisations will need to carefully consider the AI solutions they turn to for automating process-driven work.
Yet, how does this affect the security considerations required to make remote working effective for companies?
Every time we send a message to a colleague or share a company file, we share bits of data electronically.
Data, of course, is the lifeblood of every modern organisation, so when it is shared, it must be done so securely.
If we add AI into the mix, we run into a potential data security issue.
This is because, fundamentally, AI needs data to work properly.
Its purpose is to access data, analyse it and generate better outcomes for organisations through automation.
In doing so, it replaces certain tasks, but also enables employees to perform existing jobs more effectively.
Yet, despite the productivity gains, this new era of remote working doesn’t necessarily lend itself to AI being used appropriately when it comes to the secure transmission of data.
AI-driven communications are just as vulnerable to security flaws as those performed by humans.
This is especially evident when we look closely at the kind of technology enterprises use to communicate internally – a crucial component of any business model outside an office’s four walls.

According to Morten Brøgger, CEO of messaging and collaboration platform Wire, AI may not be as intertwined with the future of work as we think: “AI and the future definitely not in the collaboration and communication market or in the future of work 2.0.”
Brøgger continues: “The reason is that if you start building a lot of AI into communication tools, it means surveillance for your users, which is a clear breach of their privacy. If you do need an AI, you need to have the data to examine behavioural patterns. This means breaking end-to-end encryption, because there will be a machine that receives a copy of everything a user is doing.”
Wire is one of a crop of collaboration tools that have enabled many organisations to continue operating during COVID-19.
It found its niche by specifically targeting large enterprises back in 2017, who Brøgger believes have an advanced understanding of the importance of security and privacy.
With its leadership team comprising ex-Skype employees, the company now positions itself as being on a mission to change the way employees communicate in the workplace.
Though organisations can safely integrate AI technology, this abrupt new iteration of the workplace that we’ve suddenly found ourselves in has perhaps gone some way to exposing why AI isn’t the panacea for everything - at least not without careful security planning at the outset.

As Brøgger notes: “There were a lot of companies who were basically caught in this situation that weren’t ready for it. So, how do they put infrastructure in place that is sufficiently secure to allow people to work from home and work on things that are absolutely confidential? There are no longer any global rules – no one size fits all. That’s not how the world is, even with collaboration.”
It’s clear that the world of work is changing, though it took a pandemic to accelerate that change.

Companies need to take stock of how they can make the most of the tools available to them to reduce inefficiencies, and I still maintain that AI is, in
But it must be done in a way that doesn’t come at the expense of breaching a company’s security and putting its most valuable data assets at risk.

Alternative data and artificial intelligence - the future fuel for investors

Tomas Franczyk, Managing Director, Head of Global Information Services, Asia Pacific, Nasdaq. This is the undiscovered data from non-traditional data sources that give investors information and unique insights to help them evaluate investment opportunities.

The alternative data market, expected to become a $1.7 billion industry in the next few years, can encompass a range of sources.
These include natively digital information like web traffic, online buying habits, and social media activity, as well as more granular indicators of financial performance, such as ocean cargo and in the capital markets space, alternative data is viewed as increasingly important.
According to a 2019 survey by Greenwich Associates, 95% of trading professionals believe alternative data will become more valuable to the trading process, and 85% of banks, investors, and capital markets service providers plan to increase spending on data management.
As this new data becomes a powerful differentiator in the search for alpha, a rapidly growing community of buy-side firms have started using it to add power to quantitative and fundamental investment models with the aim of outperforming the market.
For example, Nasdaq's platform Quandl, which identifies datasets from local firms to build investment models, has partnered with large insurance companies in the United States to access insurance policies on this enables users to accurately measure car sales before automotive manufacturers report them.
This data would be extremely important, say, to hedge fund investors who need investment insights into the automotive sector.

Meanwhile, in Asia, Nasdaq is building regional-specific data products and has partnered with local fintechs and other innovative local vendors to migrate their core data and alternative data to the more sophisticated technologies mean organisations can create datasets that support managers with short-term trading strategies as well as those with a long-term approach, such as institutional investors.
Another report by Greenwich Associates last year found that 74% of firms surveyed agreed that alternative data has started to have a big impact on institutional investing, while nearly 30% of quantitative funds attribute at least 20% of their alpha to alternative data.
It can only work if it is properly interpreted and analysed.
By its nature, alternative data is harder to consume than financial data; it is often unstructured, does not follow patterns, and is created at a very fast rate.
Hence, investors now have a growing need for talent and technology, including analytics platforms, testing tools, fluid data architecture, and data science teams, to help them with their data management.
Advanced analytics and artificial intelligence (AI), such as machine learning and natural language processing, can be crucial to analysing data.

Machines can process events at roughly 2,000 times the speed of humans, digest vast datasets, and work around the clock.
During the investment process, AI-enabled data processing can increase the volume and quality of idea generation; this increase in data, including alternative data, when combined with computing power can help investment managers develop a long-lasting competitive advantage.
While some organisations are well on their way to introducing AI-based models, the industry is still understanding and identifying the operational, regulatory, and technological risks that come with the race effective risk management practices will be key for the successful adoption of AI.

Data providers have an opportunity to assist the asset management industry by making alternative data and AI the drivers of future investment research.
In fact, we may see active portfolio managers look less to the sell-side for their research needs and instead develop their own research, invest in data experts and technology, and partner with vendors to supply the information and analytical tools they need. Nasdaq offers comprehensive, bespoke, and timely data and insights to help clients build and protect assets.

Temptations - Artificial Intelligence Technology And The Price Of Admission

If your work puts you in regular contact with technology vendors, you'll have heard terms such as artificial intelligence (AI), machine learning (ML), natural language processing and computer vision before.
You'll have heard that AI/ML is the future, that the boundaries of these technologies are constantly being pushed and broadened, and that AI/ML will play an integral role in shaping this tech-forward era's most successful business models.

As a technology leader, I've heard all these claims and more. To say that AI/ML will play an increasingly impactful role in business is no overstatement. According to a recent Forbes article, the machine learning market is poised to more than quadruple in the coming years.
Many industry watchers agree that AI/ML solutions, when used to good effect, can equip your organization with a significant competitive advantage. And that makes it tempting to dive right in and start implementing these technologies without first gaining a comprehensive understanding of how they work. Accessibility to myriad options is not a barrier; almost every technology vendor now offers AI/ML services.
If anything, we are often inundated with choices in this domain. But how do we know we're making the right choices and using these services to good effect? This is where a genuine, comprehensive understanding of technology becomes critically important. For many of us, the world of AI/ML is a relatively uncharted terrain.
The answers to these fundamental questions are the keys to unlocking the true potential of AI/ML as business solutions. Current machine learning is a statistical process that employs a model/algorithm to explain a set of data and predict future outcomes. Many of these are "big data" algorithms that analyze huge quantities of data to generate predictions that are as accurate as possible.
Once we understand this, we start to see what is required to effectively use ML as a business solution. We need a lot of it, and we need it to be high quality.

Poor data quality is the biggest impediment to successfully adopting and deploying AI/ML solutions, and insufficient quantities of data can be a major hindrance as well.
Take IBM's Watson for oncology as a cautionary tale. After being trained on a small number of synthetic cancer cases, the Watson supercomputer was discovered to generate "erroneous cancer treatment advice" which ranged from incorrect to outright unsafe.
The data management process, which covers everything from data creation or acquisition to transmission and storage, is therefore intrinsically linked to AI initiatives.
When considering the cost of implementing any AI/ML solution, it's vital to also consider the cost of obtaining a robust amount of high-quality data with which to feed that solution.
Considering AI/ML Solutions In The Context Of Your Needs
Now, with a better idea of what goes into deploying AI/ML solutions, we have to consider each of our options in the context of our vision.

What do we hope to achieve by implementing AI/ML strategies?
Any AI/ML technology we implement will function within a web of our existing applications, interfaces and platforms.
So, when crafting our vision, we need to take our organization's existing technology ecosystem into consideration.
Precise goals will help us ground our vision in reality, while a more ambiguous approach may lead to equally muddled (and unsatisfactory) results.
An effective machine learning model or algorithm must, of course, continuously learn.
We won't see much success with a "set it and forget it" mentality when it comes to machine learning algorithms.
If our algorithms don't rapidly adapt to changing requirements, they quickly become irrelevant and unproductive.
It's just as imperative for an algorithm to be unbiased. Cathy O'Neil, the author of Weapons of Math Destruction, spoke to NPR about the dangers of placing blind faith in the objectiveness of ML algorithms when "we really have no idea what's happening to most algorithms under the hood."
Many of the models used today across the public and private sectors certainly suffer from the prejudices and misconceptions of their designers. In 2011, a Massachusetts man was informed his driver's license had been revoked because a facial-recognition algorithm mistook him for another Massachusetts driver who was involved in criminal activity. In a similar vein, Google's hate speech detector was reported to be racially biased.

To make the most of our AI/ML solutions, we have to invest the time and attention to governing them fairly and rigorously.
You might be excited, and reasonably so, about the seemingly boundless potential of AI/ML technology. Or maybe you subscribe to Stephen Hawking's view that the development of AI could be "the worst event in the history of our civilization." In either case, there’s no question that AI/ML technology is here to stay.