The convergence of AI and blockchain holds tremendous potential for groundbreaking advancements across various industries. By combining the intelligence of AI with the secure and decentralized nature of blockchain, new possibilities can be unlocked and innovations can be driven in industrial and daily life operations.

While AI faces challenges related to transparency and trust, blockchain can serve as a powerful tool to address these concerns. The immutable nature of blockchain’s digital records and its decentralized data storage provide a transparent and accountable framework for AI systems. By leveraging the capabilities of blockchain, stakeholders can gain insights into the decision-making process of AI algorithms and verify the integrity of the underlying data, thereby enhancing trust and facilitating explainable AI.

Moreover, the interplay between AI and blockchain extends beyond transparency and accountability. The integration of these technologies opens up new avenues for augmentation and automation. AI brings intelligence to blockchain-based networks, enabling comprehensive data analysis and actionable insights. Through automation, AI and blockchain streamline business processes, reducing friction and increasing efficiency. This seamless collaboration empowers individuals, fosters a transparent data economy, and drives the adoption of decentralized solutions.

As the intersection of AI and blockchain is delved into, the profound impact of their synergy is uncovered. A glimpse into their transformative potential is offered by the real-world use cases and interdependencies between these technologies. Through the exploration of this dynamic landscape, further exploration and innovation are sought to be inspired, ultimately harnessing the full power of the AI-blockchain relationship.

The intersection of these two technologies can bring various advancements in industrial and daily life operations. In this blog post, we will see how these two technologies together are making innovations.

AI and Blockchain Reshape Cybersecurity

AI and Blockchain together can reshape the current landscape of cybersecurity. AI can be employed in cybersecurity to detect and respond to potential threats thousands of times faster than humans. AI can spot security breaches and remove them promptly.

Blockchain can be used as a highly encrypted ledger platform to store data in a decentralized manner, allowing only authorized people to access it. Integrated with AI, Blockchain can decentralize and secure data collected by AI to enhance data privacy and safety.

With blockchain technology, all the data, variables, and processes AIs use to make decisions will be recorded in immutable records that can’t be changed. This will make it easier to audit the whole process. Real-world examples include Blackbox AI using Blockchain and machine learning to automate and speed up the construction industry’s workflow, management, and verification processes.

Supply Chain

AI and Blockchain can help improve transparency in the supply chain by allowing the tracing of products and product conditions. This prevents intermediaries from tampering with a product and improves trust among agencies. Similarly, any faulty or error during the process can be tracked and resolved timely.

AI and Blockchain allow companies to digitize physical assets and create transparent, immutable, and unalterable records. This improves management, provides greater visibility, and prevents fraud and errors. AI’s machine learning processes are crucial to supply chain management as they allow companies to forecast inventory, consumer demand, and supply chain.

Furthermore, integrating IoT (Internet of Things) with AI and blockchain can further enhance supply chain visibility. By placing IoT sensors on goods, companies can monitor their condition throughout the transportation process, and this data can be securely recorded on the blockchain. This integration allows manufacturers, freight forwarders, and port operators to have real-time insights into the movement and conditions of goods, leading to more informed decision-making and improved efficiency.

The DHL Global Trade Barometer is an example of AI and Blockchain-equipped supply chains.

Benefits of AI and Blockchain to Financial Systems

AI and Blockchain provide several solutions to the challenges faced by financial systems, providing enhanced security, improved customer service, and efficient transaction processing.

Blockchain secures transactional records by storing information in an unalterable, decentralized database visible to every network member, thus ensuring transparency and safety. A blockchain network can only be accessed with special keys. This allows only authorized people to access the information. Any attempt to tamper with the records requires authorization from all network members. AI integrated into financial systems can further improve this security by detecting real-time fraud and suspicious activities and notifying users and administration.

Blockchain and AI can be implemented to provide better customer services to clients. Various services can be automated using these technologies. Blockchain can store clients’ data securely, and AI can analyze the information and make unbiased decisions. Customer assistant chatbots are examples of such applications.

Blockchain technology has already made the traditional slow money transfer through banks much faster, cheaper, and more efficient by reducing intermediaries. AI can further enhance such systems by automating payments. With the advancements in natural language processing models, AI-powered voice assistants can facilitate payments, making transactions more convenient and accessible.

Additionally, in the realm of cryptocurrencies, AI can play a significant role in improving trading strategies and market analysis. By utilizing AI and natural language processing techniques, sentiment analysis can be conducted to gauge market sentiments towards cryptocurrencies. This analysis can help traders make informed investment decisions based on the overall positive or negative sentiment.

AI can also assist in obtaining relevant and clean data for cryptocurrency trading, allowing traders to create effective strategies. Furthermore, the integration of AI and blockchain can enable fully automated trading strategies, leveraging the speed and accuracy of AI algorithms to execute trades swiftly and profitably.

Privacy protection is another aspect where blockchain and AI complement each other. The cryptographic techniques employed in blockchain technology enhance privacy throughout the network, ensuring secure AI training and operations. Robust privacy systems enable the training and deployment of competitive and complex AI models while safeguarding sensitive data.

Healthcare

Blockchain and Artificial Intelligence are also significant in the field of healthcare. AI can rapidly perform complex computational operations and analyze large data sets without bias. Blockchain can help store medical records securely. It can improve the current EHR(electronic health record) measures by improving security and transparency. EHR software like Epic also uses artificial intelligence to predict hospital admissions, detect patient risk levels, etc.

AI-based technologies can also allow faster identification and diagnosis of a disease. Automated robots as assistants can make surgical processes much faster and more efficient. AI-based systems can also provide personalized medicine and treatment to patients.

Combination of AI and Blockchain provide a holistic approach to patient data management. This combination enables to create platforms that allows patients to securely store and control their health records while granting access to healthcare professionals for research purposes. This technology facilitates data sharing while maintaining patient privacy.

Agriculture

AI and Blockchain-based technologies also provide innovative solutions in agriculture. AI can track the growth of weeds and diseases caused by pests etc., with the help of sensors. AI can also analyze soil conditions, agriculture land appropriateness, crop timings, and other things to help farmers make better decisions.

Moreover, the integration of IoT, AI, and blockchain in agriculture enables the concept of smart farming, facilitating efficient tracking of logistics and enhancing operational processes. An exemplary application of AI and blockchain in the agricultural industry is AgrBlockIoT’s Agr-Food supply chain management solution solution, which leverages these technologies to ensure transparency and traceability.

Furthermore, the combination of AI and blockchain is highlighted for tackling challenges such as drought, desertification, pollution, and soil erosion. By embracing good agricultural practices (GAP) that prioritize food safety and sustainability, farmers can effectively manage agronomy at the field level. This includes leveraging advanced ground-based and remote sensing technologies to observe, measure, and respond to changing conditions, ultimately optimizing crop production.

Remote sensing satellites play a crucial role in agriculture by capturing high-resolution data. Through the analysis of this data, vegetation indices are derived, enabling the monitoring of changes in vegetation cover and the tracking of plant phenology. AI algorithms further contribute to agronomy management by enabling crop production forecasting, assessing crop damage, detecting nutrient deficiencies, monitoring droughts, and identifying pests and diseases.

In addition to agronomy, blockchain technology finds its application in agriculture supply chains to enhance transparency, traceability, and efficiency. By integrating blockchain with multi-agent systems (MAS), transaction costs can be reduced, logistics can be optimized, and food safety protocols can be improved. Blockchain ensures secure and transparent information management throughout the food production process, enabling traceability from seed to the final food product and improving overall supply chain efficiency.

Intellectual Property Ownership Protection:

Protecting intellectual property rights is another area where AI and Blockchain have proposed remarkable solutions. Blockchain provides an immutable ledger to track and trace a physical or digital product as ownership changes hands. There is a complete record of the chronologic history of a product, and no one can alter the records. Thus Blockchain provides transparency and privacy and makes it impossible for anyone to plagiarize someone’s content.

AI also has numerous benefits in this regard. For example, scanning billions of web pages in seconds to see if the content is plagiarized. Moreover, artificial intelligence can quickly detect and steal intellectual property or copyright infringement and notify the users.

An example is IPwe, the world’s first AI and blockchain-powered global patent registry that solves the problems like inaccurate data, outdated ownership records, and lack of transparency in the IP ecosystem.

Another example is MediChain, a healthcare-focused blockchain platform that leverages AI for intellectual property protection. MediChain enables medical professionals and researchers to securely store and share sensitive health data while maintaining control over their intellectual property. Through AI-powered algorithms, MediChain can detect and prevent unauthorized access to medical data, ensuring privacy and protecting intellectual property rights.

Conclusion

The convergence of AI and Blockchain is reshaping various industries by improving security, transparency, and efficiency.

Cybersecurity is strengthened by AI’s rapid threat detection and Blockchain’s immutable ledger. Traceability and fraud prevention has improved supply chain management. AI and Blockchain provide transparency and rapid threat detection in financial systems. Healthcare sees advancements in personalized medicine and secures patient records. Agriculture and Intellectual property protection also benefit from these technologies.

In short, integrating Blockchain and AI opens up transformative innovations across sectors.

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To learn more about variance and bias, click here and read our another article.

Ref_figure_healthcare:
Rajawat, A.S., Bedi, P., Goyal, S.B., Shaw, R.N., Ghosh, A., Aggarwal, S. (2022). AI and Blockchain for Healthcare Data Security in Smart Cities. In: Piuri, V., Shaw, R.N., Ghosh, A., Islam, R. (eds) AI and IoT for Smart City Applications. Studies in Computational Intelligence, vol 1002. Springer, Singapore. https://doi.org/10.1007/978-981-16-7498-3_12

Ref_figure_agriculture:
Bhat, Showkat & Huang, Nen-Fu & Bashir, Ishfaq & Sultan, Muhammad. (2022). Agriculture-Food Supply Chain Management Based on Blockchain and IoT: A Narrative on Enterprise Blockchain Interoperability. Agriculture. 12. 40. 10.3390/agriculture12010040.

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The 1950s — Early Days of AI

The 1950s — Early Days of AI

It all started in 1950 with Alan Turing. He was a young British polymath, who examined the mathematical prospect of AI. It was he who suggested that just like humans, machines too could use available information and reasoning to make decisions and solve problems. In his paper on creating thinking machines, titled ‘Computing Machinery and Intelligence’ in 1950, this was the logical framework based on which Turing discussed how intelligent machines can be built and their intelligence tested.

But why couldn’t Turing start work on his concepts right away? The problem was with the computers available in those days. They needed to change to facilitate such work. Prior to 1949, a precondition for intelligence was lacking in computers — they were unable to store commands; they could just execute the commands given to them. To put it differently, computers in those days could be told what to perform but they couldn’t remember what they executed. Additionally, computing was exceptionally pricey. Leasing a computer in the early 1950s would set you back by a whopping monthly amount of $200,000. Thus, testing this unfamiliar and uncertain field was affordable only for big technology companies and prestigious universities. Under such circumstances, anyone wishing to pursue AI would have needed proof of concept together with the backing of high-profile people to persuade the funding sources into investing in this endeavor.

The Conference Where It All Began

The Conference Where It All Began

It took five more years for the proof of concept to be initialized by Herbert Simon, Cliff Shaw, and Allen Newell’s program — the Logic Theorist. Funded by the RAND (Research and Development) Corporation, Logic Theorist was created to imitate a human’s problem-solving skills. Many consider it to be the first AI program, which was presented at the DSRPAI (Dartmouth Summer Research Project on Artificial Intelligence) in 1956, which was hosted by Marvin Minsky (an MIT cognitive scientist) and John McCarthy (a prominent cognitive scientist and computer scientist). It was at this conference that McCarthy coined the term ‘artificial intelligence’ and presented his thoughts in an open-ended discussion on AI by bringing together some of the top researchers from different fields.

Though McCarthy envisioned a great collaborative effort, the conference failed to meet his expectations. People attended and left the conference as they pleased, and a consensus couldn’t be reached on the standard methods that the field should use. But despite this setback, everyone enthusiastically agreed that AI was attainable. This conference was a significant milestone in the history of AI because it prompted the subsequent twenty years of AI research.

The Golden Years of AI

The Golden Years of AI

As computers became more accessible and cheaper and were able to work faster and store more information, machine learning algorithms too improved. This helped people become better at knowing which algorithm would be apt to apply in order to solve their problems. Early demonstrations like the General Problem Solver (GPS) by Newell and Simon, whose first version ran in 1957 (though work on the project continued for almost a decade), could use a trial and error method to solve a remarkable range of puzzles. But the GPS lacked any learning ability, as its intelligence was totally second-hand, and came from whatever information was explicitly included by the programmer.

In the mid-1960s, Joseph Weizenbaum created ELIZA at the MIT Artificial Intelligence Laboratory. ELIZA was a computer program designed for natural language processing between man and machine (or computers, to be specific). These successes, together with the backing of leading researchers (specifically, the DSRPAI attendees), persuaded government agencies like the DARPA (Defense Advanced Research Projects Agency) to fund AI research at numerous institutions.

It’s important to note the government’s interest was predominantly in machines that were capable of high throughput data processing as well as translating and transcribing the spoken language. There was a high degree of optimism about the future of AI but the expectations were even higher.

The First AI Winter and Subsequent Revival

The First AI Winter and Subsequent Revival

It started in the early 1970s when public interest in AI declined and research funding for AI was cut after the promises made by the field’s leading scientists didn’t materialize. More than a few reports criticized a lack of progress in this field. The first AI winter continued from 1974–80.

In the 1980s, AI research resumed when the British and U.S. governments started funding it again to compete with Japan’s efforts of becoming the global leader in computer technology with its Fifth Generation Computer Project (FGCP). By then, Japan had already built WABOT-1 (in 1972) — an intelligent humanoid robot.

AI also got a boost in the 1980s from two sources. One was attributed to David Rumelhart and John Hopfield, who popularized “deep learning” techniques that let computers learn from experience. The other was Edward Feigenbaum, who pioneered expert systems that imitated a human expert’s decision-making process.

It was in the 1980s when XCON — an Expert System of DEC, was put to use. XCON used AI techniques to solve real-world problems. By 1985, global corporations had started using Expert Systems.

The Second AI Winter

The Second AI Winter

From 1987 to 1993, the field experienced another major setback in the form of a second AI winter, which was triggered by reduced government funding and the market collapse for a few of the early general-purpose computers.

The 1990s and 2000s

The 1990s and 2000s

Several landmark goals of AI were achieved during this period. In 1997, IBM’s Deep Blue (a chess-playing computer system) defeated grandmaster Gary Kasparov, who was then the reigning world chess champion. This was a huge step forward for an AI-driven decision-making program. The same year saw the implementation of Dragon Systems’ speech recognition software on Windows. In the late 1990s, the development of Kismet by Dr. Cynthia Breazeal in the AI department of MIT was another major achievement as this artificial humanoid could recognize and exhibit emotions.

In 2002, AI entered the homes in the form of Roomba (launched by iRobot), the first robot vacuum cleaner that was commercially successful. In 2004, NASA’s two robotic geologists named Opportunity and Spirit navigated the Martian surface without human intervention. In 2009, Google began work (secretly) on developing its self-driving technology and testing its self-driven cars (which later passed Nevada’s self-driving test in 2014).

2010 to Present Day

2010 to Present Day

AI has developed by leaps and bounds to become embedded in our daily existence. In 2011, Watson — IBM’s natural language question-answering system, won the quiz show Jeopardy! by defeating two former champions, Brad Rutter and Ken Jennings. The same year, Eugene Goostman — the talking computer ‘chatbot’ captured headlines as it tricked judges during a Turing test into thinking he was human.

In 2011, Apple released Siri, a virtual assistant that NLP (natural language processing) enabled, to infer, study, answer, and suggest things to its human user while customizing the experience for every user. This was followed by similar versions of other companies in 2014 — Microsoft’s Cortana and Amazon’s Alexa.

Some other pioneering developments in the field of AI during this period were:

• Sophia — the first robot citizen (created by Hanson Robotics in 2016), which can make facial expressions, see (via image recognition), and talk via AI.
• In 2017, Facebook designed two chatbots to engage in start-to-finish negotiations with each other by using machine learning to continuously improve their negotiating tactics. But as they conversed, these chatbots diverged from human language and invented their own language to communicate, thus displaying AI to a great extent.
• In 2018, Google developed BERT, which uses transfer learning to handle a wide range of natural language tasks.

Wrapping up

Wrapping up

Today, we live in the age of big data where the rapid speed of data generation and unlimited sources facilitating data availability coupled with the massive computing power of machines, AI, and deep learning technologies have found successful applications in various domains. From banking, technology, and healthcare to marketing and entertainment, AI has achieved what once seemed to be inconceivable. The future of AI is bright as it’s poised to steadily improve further and significantly change how we live and work.

The post A Brief History Of AI first appeared on Magnimind Academy.

1- Projects come first

Having a good amount of diverse data science projects can dramatically improve the quality of your portfolio. Projects demonstrate that you have the skills and expertise to work on real-life business problems. If you’re pursuing some sort of data science program from a reputable institute, you shouldn’t have to face any problem in having projects to be solved. If you have opted for the self-learning method, you should focus on carrying out some personal data science projects to build up your portfolio.

2- Explore blogging platforms

While having your own website can surely help you develop your online presence, you should focus on getting some visibility as well. And popular blogging platforms are simply excellent for this purpose. Look for a couple of blogging platforms that get a decent amount of footfalls and come with a good tagging system that would help you reach greater audiences. Once you have your profile set up, post the successful assignments you have completed so far.

3- Have a GitHub profile

Today, Github is one of the most effective online platforms targeted at tech enthusiasts. Over the years, the platform has gained immense popularity. When you have solved a critical problem and truly want people to see the way you have done it, GitHub should be your best bet. Whether it’s a write-up or a code, drop it on the platform and share it with others. There’re lots of companies across the globe keep on looking at GitHub profiles to identify competent and genuine data science professionals.

4- Focus on social media

Having a strong presence on popular social media platforms like Twitter, LinkedIn etc can greatly help you in building a strong data science portfolio. On those platforms, you not only get chances to interact with other data science professionals and go through their inputs but can also share your insights and articles to people who may be your future employer.

Final Thoughts

When you have a strong data science portfolio, it’s up to you to opt for the way to demonstrate it to prospective employers. Depending on the data science position you’re looking it should be decided. Apart from the above tips, there’s one thing you should never overlook – the importance of practice. When people see your work and provide feedback or praise, you can rest assured of getting a bit closer to what the world calls an “expert”.

.  .  .

To learn more about data science, click here and read our another article.

The post How Do You Build A Data Science Portfolio? first appeared on Magnimind Academy.

These days, lots of resources (social media platforms being the number one) have become available to companies from where they can capture massive amounts of data. Now, this captured data is used by enterprises to develop a better understanding and closer relationships with their target customers. It’s important to understand that every new customer action essentially creates a more complete picture of the customer, helping organizations achieve a more detailed understanding of their ideal customers. Therefore, it can be easily imagined why companies across the globe are striving to leverage big data. Put simply, big data comes with the potential that can redefine a business, and organizations, which succeed in analyzing big data effectively, stand a huge chance to become global leaders in the business domain.

Structures of big data

The Structure of Big Data

Big data structures can be divided into three categories – structured, unstructured, and semi-structured. Let’s have a look at them in detail.

Structured data

It’s the data which follows a pre-defined format and thus, is straightforward to analyze. It conforms to a tabular format together with relationships between different rows and columns. You can think of SQL databases as a common example. Structured data relies on how data could be stored, processed, as well as, accessed. It’s considered the most “traditional” type of data storage.

Unstructured data

Unstructured Data

This type of big data comes with unknown form and cannot be stored in traditional ways and cannot be analyzed unless it’s transformed into a structured format. You can think of multimedia content like audios, videos, images as examples of unstructured data. It’s important to understand that these days, unstructured data is growing faster than other types of big data.

Semi-structured data

It’s a type of big data that doesn’t conform with a formal structure of data models. But it comes with some kinds of organizational tags or other markers that help to separate semantic elements, as well as, enforce hierarchies of fields and records within that data. You can think of JSON documents or XML files as this type of big data. The reason behind the existence of this category is semi-structured data is significantly easier to analyze than unstructured data. A significant number of big data solutions and tools come with the ability of reading and processing XML files or JASON documents, reducing the complexity of the analyzing process.

Conclusion

While data analytics aren’t new, the emergence of big data has dramatically changed the nature of work. It’s important for businesses looking to make most out of the big data to try to adopt advanced tools and technologies to keep up with the pace at which the data is growing.

The post What Is The Structure Of Big Data? first appeared on Magnimind Academy.

Major reasons to become a machine learning professional

Major reasons

Here’re some major forecasts about machine learning’s future.

Enhanced personalization

Personalized recommendations can entice users to complete certain actions. With the help of machine learning personalization algorithms, the information in a data can be synthesized to make appropriate conclusions like a user’s interests. For instance, on an e-commerce website, it can be deduced from a user’s browsing activity that he/she is looking for a garden chair.

Better cognitive services

Cognitive services offered by machine learning professionals all developers to incorporate intelligent capabilities into their applications. Developer can empower those applications to perform various duties including speech detection, vision recognition, speech understanding. As machine learning is evolving continuously, we can expect to see the emergence of highly intelligent applications which can increasingly see, speak, hear, and even reason with surroundings.

Fraud prevention

Machine learning professionals can analyze transactions of an e-commerce website and the machine will pick out the fraud unlike rules-based, traditional system. In the future, we’re likely to witness machine learning technology becoming more sophisticated and the machines will modify, through self-learning, to prevent fraud.

Better use of quantum computing

The machine learning spectrum can be transformed through implementation of quantum machine learning algorithms. If machine learning can be integrated into quantum computers, it could result in faster processing of data that could dramatically accelerate the ability of synthesizing information and derive insights – what the future probably holds for us.

Though many people consider machine learning to be in its nascent stage, its future is clearly bright and so is the future of machine learning professionals.

Key career opportunities as a machine learning professional

Key career opportunities

Let’s have a look the major job opportunities as a machine learning professional.

Data scientist

Professionals, who extract meaning from a massive amount of data and analyze and interpret it.

Machine learning engineer

Programmers, who develop the machines and systems that can learn and apply the acquired knowledge without requiring any specific direction or lead.

Deep learning engineer

These professionals specialize in developing tasks related to AI with the help of deep learning platforms.

Roadmap to become a machine learning professional

Roadmap

As you’re aware of the reach of machine learning, let’s discuss how you can become a machine learning professional. Though this field involves a broad range of skill sets, there’re plenty of resources like Magnimind Academy that would help you master all these skills. They offer some valuable machine learning mini bootcamps for students with different skill sets. And if you’re interested in becoming a data scientist, you can always join their full-fledged data science bootcamp in Silicon Valley to move toward that direction.

The post Prepare For The Future With Machine Learning first appeared on Magnimind Academy.

Here’re the major ways through which the finance industry is leveraging the power of artificial intelligence and machine learning.

1- Better risk management

Probably the biggest impact of artificial intelligence and machine learning on the finance industry can be found when it comes to risk management. While traditional software applications can predict creditworthiness based on the static information obtained from financial reports and loan applications, implementation of machine learning technologies can help financial institutions to go much further. Algorithms identify the signs of probable future issues and analyze a client’s history of risk cases to help the authorities make an informed decision. They’re also able to identify present market trends together with relevant news items which can affect the ability of a client to pay.

2- Improved data security

Data security has always been at the top of the list of concerns for any financial institution. And if you consider the number of data breaches occurred during recent years, there’re reasons to be concerned. Traditional security tools aren’t capable of identifying modern sophisticated cyberattacks. To mitigate security risks, financial institutions implement advanced technologies like machine learning. Security solutions powered by machine learning are come with unique abilities to secure the financial data. The combined power of big data capabilities and intelligent pattern analysis gives machine learning security technology a robust advantage over traditional tools.

3- Enhanced customer experience

Like all other industries, the financial industry is also focusing on developing the top line by implementing advanced methods to offer custom services and better experience to customers. Many financial institutions have already introduced chatbots powered by artificial intelligence abilities that can analyze the voice of a customer and converse accordingly. With the help of machine learning and big data, these chatbots understand how to respond to the questions of customers’ – from transaction-specific questions to onboarding concerns. Additionally, technologies backed by artificial intelligence and machine learning are capable of making product recommendations and handling customer requests.

Parting Thoughts

In the finance industry, the disruption triggered by artificial intelligence and machine learning is increasing exponentially and toward greater economic impact than ever, both on the customers and the industry. By addressing all the major operational aspects and adding advanced features, these technologies are not only revolutionizing the entire industry but also improving the financial health millions of customers involved in the process. And from a business perspective, these technologies are driving a more fundamental and deeper shift in the finance industry.

To learn more about machine learning, click here and read our another article.

The post How Has Machine Learning And Ai Changed And Continue To Change The Finance Industry? first appeared on Magnimind Academy.

Start with the basics

Start with the basics

The first thing you should focus on is to learn a programming language. While there’re lots of languages that you can begin with, Python is preferred by many aspiring artificial intelligence professionals because it comes with libraries which are better suited to ML (machine learning – one of the happening subsets of artificial intelligence). There’re some good ways to learn Python – from self-learning to attending a program. If you don’t have any idea about programming languages, you should go with the latter option.

Leverage the power of videos and podcasts

Leverage the power of videos and podcasts

Once you’ve obtained some programming language knowledge, the next step is listening to useful videos and podcasts related to artificial intelligence. They’ll help you gain more comprehension about the present trends and happenings in the industry, emerging technologies and how they’re being implemented in the field, their effects in the real life, and many more. Remember to get some amount of familiarity with the concepts and jargons involved as these videos and podcast often come with them mentioned.

Attend an artificial intelligence course

Attend an artificial intelligence course

This is probably the best and most effective way to learn artificial intelligence. A dedicated course on the subject will greatly help you in learning about the world of artificial intelligence. It’ll help you get immensely valuable exposure to the required skills. Usually, this type of courses brush up on the fundamentals you’ve obtained already and then help you develop the technical skills needed to work with artificial intelligence in today’s professional world.

Keep on reading articles and books on artificial intelligence

Keep on reading articles and books on artificial intelligence

While attending a guided course on artificial intelligence will prepare you to enter the professional world, lots of amazing articles and books are also available which would help you strengthen your theoretical knowledge.

Keep on practicing

Keep on practicing

Like any other field, proper practice is the best way to learn artificial intelligence. So, it’s extremely important to look for projects and obtain practical knowledge while doing them. Apart from the projects you’ll be in an artificial intelligence course, you should constantly work on other related projects not only to build your portfolio but to strengthen your knowledge about the field as well.

Final takeaway

Final takeaway

Artificial intelligence is one of the most promising technologies we’ve these days and billions of dollars are being invested in startups or artificial intelligence projects. The technology has the ability to transform almost every industry to a great extent. So, start your process of learning it as soon as possible to get prepared to join the artificial intelligence revolution.

The post What Is The Best Way To Learn Artificial Intelligence For A Starting Student? first appeared on Magnimind Academy.

But before delving deeper, let’s have a quick look at what are AI and machine learning basically.

AI and machine learning – What they are

What they are

Fundamentally, AI or artificial intelligence refers to the intelligence demonstrated by the machines. And ML or machine learning is a way using which professionals achieve AI. Machine learning can be considered as the ability of machines to learn utilizing statistical techniques without being programmed explicitly.

Ways AI and machine learning are facilitating average people’s lives

Ways AI and machine learning

The concepts of AI and machine learning aren’t completely foreign to us as they have been heavily explored by popular media. There are lots of movies which have shown us a world where AI-enabled robots and machines hold the dominating power. And these have triggered, to a good extent, lots of negative impressions about AI and machine learning among the average people. However, despite how negatively movies demonstrate the power of AI and machine learning, these technologies are truly transforming average human lives into better ones. Let’s explore the most common aspects that are being impacted by AI and machine learning.

Banking And Financial Services

Banking And Financial Services

It’s hardly possible to count the number of people that have bank accounts. In addition, just consider the number of their associated facilities like credit cards which are in circulation. Now imagine how many hours human employees of these institutions would have to invest to sift through the transactions that are performed every day? And how much time and effort it would take to identify an anomaly? With the help of AI and machine learning, a huge number of banks and financial institutions have become able to review the quality of various applications and to analyze and predict risks, in an effort to make informed decisions. The so-called traditional industry is implementing AI and machine learning to increase user engagement. High-end technologies like predictive analysis, chatbots, voice recognition etc are helping minimize the gap between potential customers and financial institutions. These days, it’s possible for any customer to contact any of these establishments anytime and from anywhere and receive real-time replies.

Healthcare Services

Healthcare Services

Both AI and machine learning have already acquired a significant part in our well-being and health. From being utilized for faster patient diagnosis to the prevention of illnesses – these technologies are being used on a regular basis by lots of healthcare service providers. These days, it’s possible to predict the potential health hazards a person may be susceptible to, depending on his/her genetic history, socio-economic status, age etc – which was simply unimaginable before the emergence of AI and machine learning. With the help of AI and machine learning-powered programs, healthcare service providers can cross-reference symptoms against databases that contain millions of cases of illnesses to expedite the process of diagnosing disease and illness, saving lives through faster and appropriate treatment. These technologies are also being adapted to expedite research works toward cures of different diseases.

EMAIL

EMAIL

Almost every person uses email these days for a huge number of purposes. It may sound unlikely but your email inbox is a place where advanced technologies take place on a regular basis. There are two key aspects where email service providers use AI and machine learning. First comes the advanced spam filter. Unlike plain rule-based filters that aren’t much effective against spam as spammers can update their messages quickly to work around them, advanced spam filters continually learn from a wide range of signals like message metadata, words in the message etc to prevent spam. Another aspect is smart email categorization. You’ve probably seen that Gmail uses an approach to categorize the emails into primary, promotion, social inboxes. This is made possible with the help of AI and machine learning together with manual intervention from users. When some messages are marked in a constant direction by a user, a real-time increment to that threshold is performed by Google in order to achieve appropriate categorization.

Transportation Industry

Transportation Industry

There’s a heavy influence of AI and machine learning on the present transportation industry can be found. These technologies have been instrumental in lowering threats triggered by reckless driving via the deployment of automation and sensory management. There are vehicles that can understand their surrounding parameters and thus, can take precautionary measures whenever needed to ensure passenger safety. Apart from vehicles, AI and machine learning technologies are to be deployed soon to prevent traffic congestion on roads and for traffic management.

Taking Over Tedious And Hazardous Jobs

Taking Over Tedious And Hazardous Jobs

AI and machine learning can seem to be a boon to humanity when we consider the fact that they liberate humans and enable them to focus on tasks in which they excel. These technologies take care of a wide range of tedious tasks that have to be performed in order to attain different results. Machines excel in performing cumbersome tasks, leaving enough time and room for humans to focus on more creative aspects of a business. In the financial sector, for example, AI and machine learning help financial analysts to get some relief from the monotonous nature of their jobs and concentrate on deeper analysis and research of all-round customer experience. In the context of hazardous jobs like bomb disposal, welding etc, AI and machine learning are helping the professionals to a great extent. These days, machines are taking over those jobs with the help of human intervention.

Social Networking

Social Networking

Almost everyone has experienced it several times. When a user uploads pictures to Facebook, the faces get highlighted automatically and the service suggests friends to tag. If you wonder how it can find out which of your friends are in the picture, Facebook uses AI and machine learning techniques to recognize faces. It also uses these technologies to personalize their users’ newsfeed and ensure that they are viewing posts that interest them. Apart from Facebook, almost all other social networking platforms including Pinterest, Instagram, Snapchat etc leverage AI and machine learning to maximize user experience.

Online Shopping

Online Shopping

Online shopping has become almost an inevitable part of life for today’s tech-savvy customers. Have you ever wondered how e-commerce websites quickly return with a collection of the most relevant items related to your search? AI and machine learning are technologies that make it possible. Personalized recommendations on their home page, product pages etc are also examples of their deployment. Fraud protection is another aspect where these technologies perform a great job. Here, AI and machine learning are deployed to not only avert fraudulent transactions but to lower the number of legitimate transactions that are declined because of being falsely marked as fraudulent.

Home Security And Home Automation

Home Security And Home Automation

When it comes to home security, these days, a significant number of homeowners are deploying cutting-edge systems are deploying high-end cameras and security systems powered by AI and machine learning. These systems are capable of building a catalog of the frequent visitors of a home and thus, can detect uninvited guests instantly. Smart homes also offer a multitude of different types of useful features such as providing notification when the kids come back from school etc. When combined with appliances, AI and machine learning can make household management and housework seamless. From allowing the refrigerator to communicate with the oven to replenishment of food and supplies – all have become possible.

Final Thoughts

From the above examples, it can be concluded that a significant number of things, which were simply unimaginable before the emergence of AI and machine learning, have become possible these days. However, similar to other technologies, AI and machine learning also come with a significant number of negative concerns. The biggest one of them is that these technologies will replace humans in performing several tasks, making people jobless eventually. However, if these technologies are looked upon as tools rather than replacements, businesses should be able to attain a huge industrial growth. According to many experts, AI and machine learning have an opportunity to work together with humans. By nature, humans are good at raising the right questions while AI and machine learning are good at dealing with huge amounts of information. By working together they can leave a huge business impact. The future of these technologies isn’t exactly clear today, but they’ll surely have an impact on society as they are doing right now. We’ll have to wait to see whether that impact turns out to be positive or negative but it can be said that these technologies have a huge potential to make the lives of the people easier to a great extent.

The post AI And Machine Learning Facilitate People’s Lives In Terms Of Many Aspects first appeared on Magnimind Academy.

However, there’re still some technical issues with virtual reality related to optimization and rendering. Until now, all advancements in the field were focused mainly on better hardware and uninterrupted and increased frame-rate. However, recently an idea of using AI for virtual reality has emerged, which will bring a multitude of benefits. The main reason is that big data and AI are perfectly suited for pattern recognition and hence, similar pattern generation. This method of working can generate a new bunch of advantages.

Over the next few years, virtual reality applications will likely to become increasingly sophisticated with the emergence of more powerful devices that are capable of developing higher quality visuals. The understanding of how we can usefully interact and navigate within virtual environments will also evolve, resulting in the development of more natural methods of exploring and interacting with virtual space. Here’re some near-coming effects of immersive virtual reality experiences boosted by the power of AI.

Hazard warnings

Hazard warnings

Apart from their own ability to judge a situation within a fraction of a second, humans have developed a diverse range of mechanisms that can help them stay safe from danger. However, these judgments that are usually called intuitions aren’t infallible.

What if a machine that has a combined experience of thousands of people could overtake such a task? Such a development can save millions of soldiers on the battlefield by helping them in anticipating the moves of opponents and alerting them in advance. AI has already been employed in different military strategies. But with this implementation, battlegrounds of the future will become a more high-tech environment.

Customized simulators

AI combined with virtual reality/augmented reality is a strong combination that can be used as a tool for educating the next generation of pilots, surgeons, among others. Today, with the help of virtual reality, we can learn to drive a car safely, without endangering our or the instructor’s life. In addition, for some activities, this also proves to be an effective way of reducing costs, as some real-life activities involve expensive supplies.

AI can replace numerous situations that occur randomly and learn from the student’s behavior. As the student gets better, the system will present increasing difficult situations. AI has the ability to improve simulated training by incorporating more data points, comparing as well as contrasting different techniques, and by personalizing the education. The improved system will act more like a customizable trainer instead of a static simulator. With a simple headset and a set of sensors, we should be in a position to learn everything. Virtually anyone should be able to get access to world-class coaching at any sporting or academic discipline.

Physical environment mapping

Physical environment mapping

Today some furniture providers offer apps that provide the users with the ability to try out furniture, after carefully inputting the size and obstacles such as doors and windows of their rooms. What if the process becomes faster and more accurate by just scanning the room with a user’s phone?

AI has the ability to help map environments in real-time and merge those results with a virtual world. The result is that users get a fully immersive virtual reality experience with real-world structures. The fledgling system comes with the ability to generate CAD-quality models of a house so that users can try decorations and furniture before they buy. With a bit more training, the system can offer on-demand design services. For instance, the users select a style and the necessary things, and the system comes up with a complete plan, much like what an interior designer does.

Game development

Game development

As a primary application of immersive technologies, it’s safe to assume that gaming will continue to be one of the major driving forces for virtual reality’s progression, and in this endeavor, AI can help to a great extent. First, it’ll replace the present method of animation. Right now, two methods are applied for animating characters – manual CG work and motion capture.

Motion capture is restricted to the physical capabilities of the actor while handcrafted animations are highly laborious. Motion capture involves recording a huge array of movements which are essentially repeated time and time again. New systems utilize machine learning to merge a huge library of the stored movements and then map them onto characters that are being developed. This’ll open up a new domain of realistic animation in the context of cartoons, video games, and virtual reality environments. Even non-player characters may become part of the story in a more believable and relatable way.

Immersive traveling experience

Immersive traveling experience

Virtual reality isn’t only about beautiful worlds where people can lose themselves. It can also come up with an amazing replica of locations in the real world that are costly or somewhat impossible to reach for the common people.

Development of immersive travel experiences can be as close as it gets to the actual thing for some demographics. It can also become a new type of entertainment for people who’re passionate about traveling.

True socializing

True socializing

Facebook’s heavy invest in virtual reality with its acquisition of Oculus Rift, we’ve already received a hint about that one day, social media will likely to get a boost from the virtual reality immersive experiences powered by AI.

In the future, AI may have the task of designing users’ social media avatar by considering both their pictures and preferences. In the near future, we may be in a position to meet our friends in virtual environments. The concept requires mind-boggling processing power, but AI together with virtual reality has the ability to make it possible.

Rendering optimization

Rendering optimization

One of the major challenges in virtual reality/augmented reality is delivering realistic graphics with present day’s consumer hardware. A huge amount of complexity results into lag and pixelated images that in turn results into problems for virtual reality headset wearers. As a result, most of the virtual reality experiences available today are lacking in convincing detail and simplistic.

However, in virtual reality, AI techniques can be used for selective rendering where only some specific portions of a scene are dynamically generated. AI techniques can also help to compress images intelligently, enabling faster transmission over wireless connections without any understandable loss in quality.

Challenges with more immersive content

Challenges

Implementation of AI for virtual reality/augmented reality is expected to offer more immersive technology which will be increasingly personalized. The drive to capture people’s attention generates two challenges. First, a lack of authority over personal data may drive the users away from the long-term adoption of the new technologies. User privacy and data controls have become key concerns for customers. Given the improved data tracking features of immersive technologies, from tracking facial expressions to eye-movements, the personal data will become at more risk, making privacy a more serious concern. Secondly, the well-being of the users will become at stake. Let’s have a look at some probable steps that can be taken to mitigate these challenges.

More Authority To Users About Their Personal Data

More Authority To Users About Their Personal Data

It’s a fact that major virtual reality companies use cookies to store data, while collecting information on the browser and device type, location, among others. In addition, communication with other users in virtual reality environments is being stored and sometimes shared with third parties for marketing purposes. It leads to the necessity of a solution that acts like a buffer between companies and users.

Regulatory Frameworks

Regulatory Frameworks

The privacy concerns associated with traditional media has already started arising in immersive content. If developers aren’t willing to provide agreeable and clear terms of use, regulators need to step in to protect the consumers, as already done by some jurisdictions.

As companies develop advanced applications using immersive technologies, they should focus on the transition from using metrics that only measure the amount of user engagement. Alternative metrics may include something like a net promoter score for the software that would indicate how strongly consumers recommend those services to their friends based on their own experience with them.

Final thoughts

Lagging hardware and costly barriers have caused virtual reality to become overhyped over the last few years. With the implementation of AI, organizations can overcome earlier technical barriers while improving realism. These are only some of the possible applications of AI in virtual reality.

As the technology becomes more widely accepted, we can expect to see more innovative applications in the near future. However, more work on the part of the developers will be required if immersive technologies are to generate more interactions with the content and media.

The post Immersive Virtual Reality AI And Its Near-coming Effects first appeared on Magnimind Academy.

However, there’re lots of people who’re understandably concerned about the actual impact of artificial intelligence – whether it’s good for the society or not. Finding a proper answer to this question has become even more important as there’re individuals who’re deeply skeptical about AI’s potential and are wary of how the technology is going to impact the society.

Like any other technological aspect, the debate is complex for AI too. And of course, there’re benefits and issues along the way, as artificial intelligence becomes more pervasive in human lives.

We should always remember that when there’re challenges, there’re some good opportunities too. And we can’t afford to look at AI with skeptical eyes. Rather, we’ve to understand the benefits it has already offered to our society.

Major societal benefits of AI

Major societal benefits of AI

Some of the key societal benefits that artificial intelligence has brought along are outlined below.

Healthcare

Healthcare

Healthcare has always been one of the focal points of AI. It boasts of a huge amount of data to populate and analyze based on which computational sophistication has been improved by designers.

For instance, Merantix, a German company, applies deep learning to medical issues. It offers an application capable of detecting lymph nodes in the human body in CT (Computer Tomography) images. If the detection is done by humans, the charge would be prohibitively expensive. In this scenario, deep learning trains computers on datasets to learn what an irregular-appearing versus a normal-looking lymph node is. Once done, radiological imaging specialists apply this knowledge to real patients and identify the extent to which somebody is at risk of carcinogenic lymph nodes, at a significantly lower cost.

AI tools can predict substantial challenges lying ahead in advance and offer resources for patient education and proactive interventions, thus helping people to maintain their wellbeing.

Transportation

Transportation

Transportation is a field where artificial intelligence together with machine learning has produced major innovations. Autonomous vehicles like cars, buses, trucks etc use advanced capabilities that offer features like lane-changing systems, automated vehicle guidance, automated braking, use of sensors and cameras for collision avoidance, and analyzing information in real time by using AI, among others.

For instance, AI and LIDARs (light detection and ranging systems) play key roles in collision avoidance and navigation. These instruments provide information that helps to keep fast-moving vehicles in their designated lanes, thus helping them avoid other vehicles and applying brakes when needed etc, thus and ultimately saving human lives by reducing road accidents.

Prediction Of Natural Disasters

Prediction Of Natural Disasters

AI is considered as one of the perfect sources of predicting natural occurrences. There’s an AI model that can almost perfectly guide you what the weather will be for the next couple of days, which was almost unimaginable before the advent of artificial intelligence.

There’s also an incredible system that can predict, based on the simulation of tectonic plates of the earth, the time of volcanic eruptions. There’re AI-enabled projects that gather data for magnetometers of the phone and send it for analysis based on which successful predictions about earthquake can be made.

Farming

Farming

Farming is another sector that has been heavily benefitted from AI. This is an industry full of challenges like competition for natural resources, plateauing agricultural productivity, and rapidly growing population.

In this scenario, consider FarmLogs, a farming management app presently used by many farmers in the US, which uses technology and data to help farmers track the weather, monitor fields, obtain insights into soil utilizing historical satellite imagery, and even identify irregular plant growth. Real-time data analytics help farmers to maximize their crop yields and thus, in turn, their profits too.

Strengthened Economy

Strengthened Economy

Probably all of us have seen headlines that state something like adoption of AI will lead to unemployment. In reality, this is far from the truth. Artificial intelligence promotes a gradual evolution in the job field, which will be positive with the companies planning ahead.

Humans will still work, but they’ll be working more efficiently with the help of AI. Besides this unparalleled combination of machine and human, there will be a natural requirement of trained people who’ll be supervising the systems, apart from those who’ll actually do certain jobs. It’ll gradually result into more job openings, thus solidifying the economy.

Smart Cities

Smart Cities

AI is being implemented by various authorities to optimize different facilities. Artificial intelligence is considered as a way to deal with large volumes of data and to identify efficient ways of responding to various public requests. Instead of addressing service issues in ad hoc manners, authorities are implementing AI to be proactive in how urban services can be provided.

Smart city applications often use artificial intelligence to improve environmental planning, service delivery, energy utilization, resource management, and crime prevention, among others. Some of the top applications include intelligent traffic signals, e-governance applications, smart meters for utilities, Wi-Fi kiosks etc.

Overall Lifestyle

Overall Lifestyle

With the increasing implementation of AI in different segments of society, overall lifestyle of the humans gets enhanced. Some of the mundane tasks such as data entry or answering emails can be performed by intelligent assistants, freeing up precious time for humans to focus on creative aspects of the work.

Smart homes can be made capable of providing better security and reducing energy usage that would greatly promote the concept of a greener environment.

Steps we should take

With all the AI benefits, there comes some significant disadvantages as well, but that’s natural for any technology.

The question that is making lots of people worried is this: With too much of authority given to the machines, how can implementation of AI be made more favorable to the society? Or, at the least, how to make it not act like a threat to human life and property?

Transparency Is The Key

Transparency Is The Key

Results can be questioned even with a greatly planned decision-making system, if the reasoning can’t be demonstrated. For example, if AI has diagnosed an illness, the patient can always ask for a proper reasoning, failing which would lead to non-transparency of the system.

Assuming that artificial intelligence will be making hugely important decisions, implementation of AI has to be perfectly planned and the results have to be transparent and explainable to be accepted by the society. In addition, AI should be use data science to improve the living conditions.

Importance Of Ai Curriculum

Artificial intelligence should be used where it’s more effective and efficient to employ a machine to handle the task compared to engaging a human brain to perform it. So, companies should ensure scenarios where success gets replicated in raising the technology, in terms of both costs and resources.

High-quality protocols should also be developed by data scientists when it comes to selecting training data and taxonomies for AI. If it’s trained with patchy, skewed or flawed examples, the results will be going to be unreliable. So, it also needs to be ensured that the data is relevant, appropriate, accurate, diverse, accurately labeled, and representative.

For instance, if an AI-enabled system for hiring recommendations is trained on the data of present and past employees solely, and those employees aren’t diverse (e.g. predominantly older white females), the resulting model would likely be biased unfairly against candidates who’re young, racial minorities, and male. Even after an AI-enabled system is launched, the risk and quality of unfair bias need to be assessed by human overseers in ongoing new training data.

In addition, as we aim to improve the fairness and efficacy of AI with proper training data to benefit the society, we should also keep in mind that fundamental privacy principles are closely related to the massive amounts of data used by AI. Usage and retention of personal data needs to be minimized, while limiting the ways in which that data could be used in the future. Big data analytics solutions include the remedy of these problems.

Final takeaways

It’s important to note that AI isn’t able to learn on its own and thus, humans are required to help any type of artificial intelligence obtain a better understanding of all types of jobs, processes, things etc. When it comes to maximizing societal benefits, perhaps the best approach to leverage both AI systems and human-only systems is to do what each of them does best. Leveraging artificial intelligence as well as the best of human ability and values promises greater progress in accountability, transparency, and fairness. And this will be playing a crucial role in building a strong trust for AI in the society.

For instance, AI can be put to work to do the time-consuming analysis of the huge amount of information available. Building a culture of continuous learning and collaboration is crucial to take maximum advantage of artificial intelligence. This combined approach is what will make processes and people even more important than they are today. And to make the most out of the technology, society needs to deploy AI that puts humans first, protects human rights, and fosters humans’ trust. Immersive data science experience is proof that we can rely on artificial intelligence.

The post Invaluable Societal Benefits Of AI first appeared on Magnimind Academy.