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Amid global crypto fall, this altcoin jumped over 50% in mcap to surpass Shiba Inu – Financial Express

The jump in Avalanche has reportedly come on the back of its developer Ava Labs partnership with Deloitte. (Image: Pexels)

Even as the crypto world witnessed turbulence in the past nearly two weeks with over $300 billion lost in market value, Avax the native token of smart contract blockchain platform Avalanche has been a big outlier. The altcoins price has jumped from around $90 to $137, at the time of filings this report, data from CoinMarketCap showed. The market cap was up 51 per cent from around $20 billion to $30.34 billion on Sunday afternoon, making it the 11th largest crypto in the world and surpassing Shiba Inus $25.4 billion market cap.

The decline in the crypto world over the past seven days involved all leading coins including Bitcoin, which had slipped below $60,000 market, except Tether and USD Coin the fourth and nineth largest cryptos respectively that have remained in green.

Avalancheis alayer one blockchainthat functions as a platform for decentralised applications and custom blockchain networks,according to CoinMarketCap. Importantly, the price-tracking website said that it is one of Ethereums rivals, aiming to unseatEthereumas the most popular blockchain forsmart contracts. Avalanche aims to do so by having a higher transaction output of up to 6,500 transactions per second while not compromising scalability.

Also read:Bitcoin slips below $60,000 after record surge; Ethereum, other altcoins also see double-digitdrop

The jump in Avalanche has reportedly come on the back of its developer Ava Labs partnership with Deloitte. Ava Labs is thrilled to announce its partnership with Deloitte to build more efficient disaster relief platforms using the Avalanche blockchain. This effort combines the speed, resilience and adaptability of Avalanche, and Deloittes Fortune 100 enterprise knowledge, Ava Labs co-founder and Cornell University professor Emin Gn Sirer said on Twitter last Tuesday.

The new platform gives state and local officials a decentralised, low-cost and fully immutable system that empowers both the grant funders, and the aid recipients, while using the transparency of blockchain to minimize fraud, waste and abuse,Sireradded. Last Wednesday,Binance.US the US arm of crypto exchangeBinance, had announced that it would list Avalanche on November 18 and open trading on open trading onAVAX/USDandAVAX/USDT. According to CoinMarketCap, the total supply of AVAX is 720 million.

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Amid global crypto fall, this altcoin jumped over 50% in mcap to surpass Shiba Inu - Financial Express

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Crypto Giant Coinbase Lists Ethereum-Based Exchange Altcoin on Retail Trading Platform – The Daily Hodl

Another Ethereum-based exchange altcoin is now available across crypto giant Coinbases full retail platform.

Coinbases customers can now buy, sell and store Voyager Token (VGX), the native token of centralized exchange Voyager, on Coinbase.com and the exchanges mobile apps, according to a new announcement from the company.

Coinbase Pro listed VGX on Wednesday. The crypto asset is up 41% in the past seven days but down nearly 10% in the past 24 hours. Its trading at $3.83 at time of writing, down from a weekly high of $4.29, which it hit earlier in the day on Friday.

Voyager says that VGX is a liquid token that provides utility across its platform. Participants can earn interest by staking VGX or using the token to enable instant transactions, receive fee discounts and earn rewards for trading.

Voyager itself is a mobile broker that supports the trading of more than 60 crypto assets.

Coinbase added support for VGX amid a flurry of new altcoin listings on Coinbase Pro. The exchanges chief executive, Brian Armstrong, noted in Julythat they plan to list as many altcoins as possible.

Reminder about how Coinbase lists assets: our goal is to list *every* asset where it is legal to do so.

Featured Image: Shutterstock/Kit8.net

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Crypto Giant Coinbase Lists Ethereum-Based Exchange Altcoin on Retail Trading Platform - The Daily Hodl

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SOL, DOT, XRP, ONE, and FTT Price Poised For Breakout! Here are Levels to Watch – Coinpedia

The November month has been on the skids for Crypto space with no significant gains. Though market leaders Bitcoin and Ethereum failed to reach the markets port of call, top-tier altcoins seek hard to uplift the broader market cap. Notably, Solana, Polkadot, Harmony, FTX Token, and Ripple exhibits the virtue of breakout this week.

The Solana price has made a breathtaking move in the second half of the year, with a more than a 500% surge. Consequently, the altcoin remained persistent since the start of Q4 managing the uptrend. The protocol has been trading within a tight symmetrical triangle. The asset may experience a slight pullback between $207 to $219 to confirm the pattern before clinching a high above $235 by the weekend.

Polkadot has been one of the traders favorite coins in recent days owing to its initiation of the Parachain auction. The DOT price has seen a dramatic rise post the announcement, claiming a new ATH of $55 on 8th November. The asset has been accumulating an effective bullish pennant channel, an additional drop up to $37.65 would help the solid breakout. The significant surge in the buy orders would exhilarate the price up to $50 by the weekend.

The top-tier altcoin has been peddling hard times with SECs prevailing lawsuit against it. Yet the XRP price chart indicates the probable breakout of the solid falling wedge pattern. The four-hour chart from the trading view displays the close fight between buyers and sellers. An increase in the buy orders in the next couple of days would initially uplift the price to $1.18. Further, a continued predominance of bulls would lead the price to $1.30 by the weekend.

The Harmony price has undergone an exponential growth post the commencement of the second half of the year. The ONE price seems to be taking a short breather after claiming an ATH of $0.3798 on 26th October. The protocol has been following a tight symmetrical triangle with an equal fight between buyers and sellers. A chunk of sellers moved their bags in the past couple of hours. If the asset continues to plummet up to $0.26 then breakout would kick the price action close to the previous ATH of $0.35.

The FTT price has followed an ebb and flow pattern with notable gains since the start of the quarter. The token has been trading within a tight falling wedge channel. With little more strength above the $50.5 levels, the price would initially pass the retest at $55. Further preserving the uptrend, the price would close the week at $60.

Collectively, the broader market cap displays slow recuperation post the substantial drop over the past week. Meanwhile, the aforementioned coins gain strength at their crucial zone to ride the week with positive gains. However, if market sentiments favor the above coins, then breaking out to mentioned levels looks imminent.

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SOL, DOT, XRP, ONE, and FTT Price Poised For Breakout! Here are Levels to Watch - Coinpedia

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With Bitcoin, Ethereum in the picture, can MATIC pull off a recovery – AMBCrypto

The beginning of November was still a relatively good time for the market as most top coins hit all-time highs and the market cap glimmered. MATIC, however, has been on a downtrend for over 20 days now.

With the larger market correcting across the board, MATICs momentum slowed down even further. In fact, its price has fallen by over 20% since.

MATICs price has been on a downtrend since 1 November, losing over 22% of its value after testing a multi-week high of $2.22 on 29 October. Further, while most of the top alts charted ATHs, MATICs price fell just short of it.

In fact, with most altcoins market cap rising, the crypto also lost its rank in the top twenty assets by market cap.

Source: Trading View

Nonetheless, despite low expectations, MATICs social volumes rose after the news of the launch of an advanced ZK-based scaling solution. The solution is expected to improve the validation process for DApps on the Polygon network. Many believe this could potentially address Ethereums network congestion issues, while also boosting the networks utility.

Source: Sanbase

Whats more, just recently, Polygon also announced that it would be able to offer faster and secure atomic swaps between Bitcoin and other assets after forming a strategic partnership with Portal. Thanks to news of these associations, can MATICs price see growth like its social volumes did?

Well, first of all, MATICs price needs to establish itself above the $1.7-mark. A retail push from this level to its next major resistance level would aid the alts run.

Total value locked (TVL) in Polygon, after more than two months of maintaining itself in the $4.5 $5 billion range, dropped to $4.2 billion this week.

On the face of it, this may be considered a bad sign for the networks future. However, Polygon noted a spike in increasing new addresses, peaking at 1.9 million addresses per day.

Source: Coin98Analytics

Notably, MATICs 7-day MVRV returned to the positive zone, but with declining price action, the same seemed to be turning towards the overheated zone at press time.

On the other hand, its 30-day MVRV was still flashing low values of -8.8% and didnt seem to be on a return trip yet.

Source: Sanbase

Towards the end of last month, net inflows on MATIC multiplied as the protocols revenue hit a new all-time high. At the moment, the crypto lacks the same momentum. With trade volumes on a downtrend, seems like the altcoin will need more support from the retail side before it begins to rally or even recover.

While price-wise MATICs recovery looks slow, it isnt absent altogether. However, despite the recent bullish news, it seems like this recovery could use some more support from traders and a stronger network to rally going forward.

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Defining what’s ethical in artificial intelligence needs input from Africans – The Conversation Africa

Artificial intelligence (AI) was once the stuff of science fiction. But its becoming widespread. It is used in mobile phone technology and motor vehicles. It powers tools for agriculture and healthcare.

But concerns have emerged about the accountability of AI and related technologies like machine learning. In December 2020 a computer scientist, Timnit Gebru, was fired from Googles Ethical AI team. She had previously raised the alarm about the social effects of bias in AI technologies. For instance, in a 2018 paper Gebru and another researcher, Joy Buolamwini, had showed how facial recognition software was less accurate in identifying women and people of colour than white men. Biases in training data can have far-reaching and unintended effects.

There is already a substantial body of research about ethics in AI. This highlights the importance of principles to ensure technologies do not simply worsen biases or even introduce new social harms. As the UNESCO draft recommendation on the ethics of AI states:

We need international and national policies and regulatory frameworks to ensure that these emerging technologies benefit humanity as a whole.

In recent years, many frameworks and guidelines have been created that identify objectives and priorities for ethical AI.

This is certainly a step in the right direction. But its also critical to look beyond technical solutions when addressing issues of bias or inclusivity. Biases can enter at the level of who frames the objectives and balances the priorities.

In a recent paper, we argue that inclusivity and diversity also need to be at the level of identifying values and defining frameworks of what counts as ethical AI in the first place. This is especially pertinent when considering the growth of AI research and machine learning across the African continent.

Research and development of AI and machine learning technologies is growing in African countries. Programmes such as Data Science Africa, Data Science Nigeria, and the Deep Learning Indaba with its satellite IndabaX events, which have so far been held in 27 different African countries, illustrate the interest and human investment in the fields.

The potential of AI and related technologies to promote opportunities for growth, development and democratisation in Africa is a key driver of this research.

Yet very few African voices have so far been involved in the international ethical frameworks that aim to guide the research. This might not be a problem if the principles and values in those frameworks have universal application. But its not clear that they do.

For instance, the European AI4People framework offers a synthesis of six other ethical frameworks. It identifies respect for autonomy as one of its key principles. This principle has been criticised within the applied ethical field of bioethics. It is seen as failing to do justice to the communitarian values common across Africa. These focus less on the individual and more on community, even requiring that exceptions are made to upholding such a principle to allow for effective interventions.

Challenges like these or even acknowledgement that there could be such challenges are largely absent from the discussions and frameworks for ethical AI.

Just like training data can entrench existing inequalities and injustices, so can failing to recognise the possibility of diverse sets of values that can vary across social, cultural and political contexts.

In addition, failing to take into account social, cultural and political contexts can mean that even a seemingly perfect ethical technical solution can be ineffective or misguided once implemented.

For machine learning to be effective at making useful predictions, any learning system needs access to training data. This involves samples of the data of interest: inputs in the form of multiple features or measurements, and outputs which are the labels scientists want to predict. In most cases, both these features and labels require human knowledge of the problem. But a failure to correctly account for the local context could result in underperforming systems.

For example, mobile phone call records have been used to estimate population sizes before and after disasters. However, vulnerable populations are less likely to have access to mobile devices. So, this kind of approach could yield results that arent useful.

Similarly, computer vision technologies for identifying different kinds of structures in an area will likely underperform where different construction materials are used. In both of these cases, as we and other colleagues discuss in another recent paper, not accounting for regional differences may have profound effects on anything from the delivery of disaster aid, to the performance of autonomous systems.

AI technologies must not simply worsen or incorporate the problematic aspects of current human societies.

Being sensitive to and inclusive of different contexts is vital for designing effective technical solutions. It is equally important not to assume that values are universal. Those developing AI need to start including people of different backgrounds: not just in the technical aspects of designing data sets and the like but also in defining the values that can be called upon to frame and set objectives and priorities.

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Artificial Intelligence and Machine Learning, Cloud Computing and 5G Will be the Most Important Technologies in 2022, Says New IEEE Study – APN News

Published on November 23, 2021

Chief information officers, chief technology officers and technology leaders globally surveyed on key technology trends, priorities and predictions for 2022 and beyond

IEEE, the worlds largest technical professional organization dedicated to advancing technology for humanity, today released the results of The Impact of Technology in 2022 and Beyond: an IEEE Global Study, a new survey of global technology leaders from the U.S., U.K., China, India and Brazil. The study, which included 350 chief technology officers, chief information officers and IT directors, covers the most important technologies in 2022, industries most impacted by technology in the year ahead, and technology trends through the next decade.

The most important technologies, innovation, sustainability and the future

Which technologies will be the most important in 2022? Among total respondents, more than one in five (21%) say AI and machine learning, cloud computing (20%) and 5G (17%) will be the most important technologies next year. Because of the global pandemic, technology leaders surveyed said in 2021 they accelerated adoption of cloud computing (60%), AI and machine learning (51%), and 5G (46%), among others.

Its not surprising, therefore, that 95% agree including 66% who strongly agree that AI will drive the majority of innovation across nearly every industry sector in the next 1-5 years.

When asked which of the following areas 5G will most benefit in the next year, technology leaders surveyed said:

telemedicine, including remote surgery and health record transmissions (24%)

remote learning and education (20%)

personal and professional day-to-day communications (15%)

entertainment, sports and live event streaming (14%)

manufacturing and assembly (13%)

transportation and traffic control (7%)

carbon footprint reduction and energy efficiency (5%)

farming and agriculture (2%)

As for industry sectors most impacted by technology in 2022, technology leaders surveyed cited manufacturing (25%), financial services (19%), healthcare (16%) and energy (13%). As compared to the beginning of 2021, 92% of respondents agree, including 60% who strongly agree, that implementing smart building technologies that benefit sustainability, decarbonization and energy savings has become a top priority for their organization.

Workplace technologies, Human Resources collaboration and COVID-19

As the impact of COVID-19 varies globally and hybrid work continues, technology leaders nearly universally agree (97% agree, including 69% who strongly agree) their team is working more closely than ever before with Human Resources leaders to implement workplace technologies and apps for office check-in, space usage data and analytics, COVID and health protocols, employee productivity, engagement and mental health.

Among challenges technology leaders see in 2022, maintaining strong cybersecurity for a hybrid workforce of remote and in-office workers is viewed by those surveyed as challenging by 83% of respondents (40% very, 43% somewhat) while managing return-to-office health and safety protocols, software, apps and data is seen as challenging by 73% of those surveyed (29% very, 44% somewhat). Determining what technologies are needed for their company in the post-pandemic future is anticipated to be challenging for 68% of technology leaders (29% very, 39% somewhat). Recruiting technologists and filling open tech positions in the year ahead is also seen as challenging by 73% of respondents.

Robots rise over the next decade

Looking ahead, 81% agree that in the next five years, one quarter of what they do will be enhanced by robots, and 77% agree that in the same time frame, robots will be deployed across their organization to enhance nearly every business function from sales and human resources to marketing and IT. A majority of respondents agree (78%) that in the next 10 years, half or more of what they do will be enhanced by robots. As for the deployments of robots that will most benefit humanity, according to the survey, those are manufacturing and assembly (33%), hospital and patient care (26%) and earth and space exploration (13%).

Connected devices continue to proliferate

As a result of the shift to hybrid work and the pandemic, more than half (51%) of technology leaders surveyed believe the number of devices connected to their businesses that they need to track and manage such as smartphones, tablets, sensors, robots, vehicles, drones, etc. increased as much as 1.5 times, while for 42% of those surveyed the number of devices increased in excess of 1.5 times.

However, the perspectives of technology leaders globally diverge when asked about managing even more connected devices in 2022. When asked if the number of devices connected to their companys business will grow so significantly and rapidly in 2022 that it will be unmanageable, over half of technology leaders disagree (51%), but 49% agree. Those differences can also be seen across regions 78% in India, 64% in Brazil and 63% in the U.S. agree device growth will be unmanageable, while a strong majority in China (87%) and just over half (52%) in the U.K disagree.

Cyber and physical security, preparedness and deployment of technologies

The cybersecurity concerns most likely to be in technology leaders top two are issues related to the mobile and hybrid workforce including employees using their own devices (39%) and cloud vulnerability (35%). Additional concerns include data center vulnerability (27%), a coordinated attack on their network (26%) and a ransomware attack (25%). Notably, 59% of all technology leaders surveyed currently use or in the next five years plan to use drones for security, surveillance or threat prevention as part of their business model. There are regional disparities though. Current drone use for security or plans to do so in the next five years are strongest in Brazil (78%), China (71%), India (60%) and the U.S. (52%) compared to only (32%) in the U.K. where 48% of respondents say they have no plans to use drones in their business.

An open-source distributed database that uses cryptography through a distributed ledger, blockchain enables trust among individuals and third parties. The four uses in the next year respondents were most likely to cite in their own top three most important uses for blockchain technology are:

Secure machine to machine interaction in the Internet of Things (IoT) (61%)

Shipment tracing and contactless digital transactions(51%)

Keeping health and medical records secure in the cloud (47%)

Securing connecting parties within a specified ecosystem (47%)

The vast majority of those surveyed (92%) believe that compared to a year ago, their company is better prepared to respond to a potentially catastrophic interruption such as a data breach or natural disaster. Of that majority, 65% strongly agree that COVID-19 accelerated their preparedness.

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McGill Artificial Intelligence Society’s panel discusses ethics and regulation of artificial intelligence – McGill Tribune

The McGill Artificial Intelligence Society (MAIS) held its first in-person event of the school year, a panel titled Ethics in AI, on Nov. 17. The audience was at full capacity, drawing in a crowd of approximately 35 people from the McGill community to the Trottier lecture hall.

The panel featured three professionals who engage with issues surrounding AI ethics in their respective disciplines: Masa Sweidan, McGill alumna and business development manager at the Montreal AI Ethics institute (MAIEI); Ignacio Cofone, assistant professor of privacy law, AI law, and business associations at McGill; and Mark Likhten, legal innovation lead at Cyberjustice Lab at LUniversite de Montreal (UdeM).

Kaustav Das Sharma, U4 Engineering and team lead of the McGill AI Podcast, moderated the event. The panellists acknowledged that AI is often misrepresented in media and popular culture, and agreed that it is important for the public to gain a more holistic understanding of AI and the ethical barriers that emerge with its advancement.

What is important is to [] be clear about what AI is, Likhten said. It is a very powerful tool, but its still a tool which needs [] human intervention.

Cofone considered more precise issues, namely bias, transparency, and privacy within AI, as the issues that should garner more public attention. These three issues are at the core of his research in AI regulation.

One important aspect to be aware of [] is AI bias, Cofone said. AI decision-making affects everyone, everyday [.] Transparency [in AI] is important particularly with decision-making processes such as calculating credit scores to see if you would get a house [or] calculating your risk score to see if you go to jail [.] Privacy is important because most AI is trained with [sensitive] information about us.

There was also discussion regarding how public institutions can work to push inclusivity and diversity to the forefront of AI research and development. Sweidan stated that diversifying AI education is a crucial first step.

Having education that includes women, BIPOC, and LGBTQ [communities] is extremely important, Sweidan said. Having people with different backgrounds, looking at it from the philosophy standpoint, [from computer science], from law, I think that is what leads to a more holistic education, and I think that is an extremely important first step.

Panellists also discussed the potential of AI systems to inflict harm, and the importance of adequate personal data protection regulation. Ending on a positive note, Das Sharma asked panellists what makes them excited for a future blossoming with cutting-edge AI development. Sweidan said that she is excited by the possibilities for AI creativity, while Likhten cited the applications of AI in justice.

I am actually very optimistic for the future, said Likhten. [Cyberjustice Lab] works a lot with tools [using] AI to improve access to justice, and the possibilities that we see in that field are endless [.] We talk a lot about people getting stripped of their personal data [], and the bad sides of AI, but there are lots, and lots of [] good things that you can do with AI that remain within the boundaries of ethical principles.

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Staten Island Family Advocating For New Artificial Intelligence Program That Aims To Prevent Drug Overdoses – CBS New York

NEW YORK (CBSNewYork) So many families have felt the pain of losing a loved one to a drug overdose, and now, new artificial intelligence technology is being used to help prevent such tragedies.

When you have a family member who lives this lifestyle, its a call you always know could come, Megan Wohltjen said.

Wohltjens brother, Samuel Grunland, died of an overdose in March 2020, just two days after leaving a treatment facility. He was 27.

Very happy person. He was extremely athletic. Really intelligent, like, straight A student He started, you know, smoking marijuana and then experimenting with other drugs, Wohltjen told CBS2s Natalie Duddridge.

He wanted to get clean and addiction just destroyed his life, said Maura Grunland, Sams mother.

Since Sams death, his mother and sister have been advocating for a new program they believe could have saved him. Its called Hotspotting the Opioid Crisis.

Researchers at MIT developed artificial intelligence that aims to stop an overdose before it happens.

This project has never been tried before, and its an effort to combine highly innovative predictive analytics and an AI-based algorithm to identify those who are most at risk of an overdose, said former congressman Max Rose, with the Secure Future Project.

The technology screens thousands of medical records through data sharing with doctors, pharmacies and law enforcement.

For example, over time, it might flag if a known drug user missed a treatment session, didnt show up to court or, in Sams case, just completed a rehab program. It then alerts health care professionals.

Im just calling to check in to see how things are going, said Dr. Brahim Ardolic, executive director of Staten Island University Hospital.

Ardolic says the program trains dozens of peer advocates who themselves are recovering addicts. They reach out to at-risk individuals and find out what they need from jobs to housing to therapy.

Theres no pressure on the patient to enter rehab. The goal is to keep them alive.

We cant help them if theyre dead If youre not ready for treatment, you should be ready for harm reduction. You should have Narcan available if you or a friend overdoses, Ardolic said.

Health officials say a record number of people, 100,000, died of overdoses in 2020.

This year alone on Staten Island, more than 70 people have fatally overdosed.

The number of opioid deaths per 100,000 people on Staten Island is about 170% higher than the national rate. Officials say fentanyl is largely to blame, and the lethal drug was found in 80% of Staten Island toxicology reports.

I believe that my son would be alive today if he hadnt used fentanyl I really feel that if this was any other disease, people would be up in arms, Maura Grunland said.

Wohltjen says her brother always encouraged her to run the New York City Marathon, so this year, she did it, wearing his Little League baseball hat and raising thousands of dollars for the Partnership to End Addiction.

If we could save one life it would make a difference, Wohltjen said.

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Data Scientist vs. Artificial Intelligence Engineer: Which Is a Better Career Choice? – Latest Digital Transformation Trends | Cloud News – Wire19

Data science and artificial intelligence are commonly used interchangeably in todays digital environment, but theyre not the same thing. AI and Data Science are two skill-based career pathways that cross frequently. With rich salaries and satisfying rewards, data science and artificial intelligence are the rocket ships blasting off in the post-pandemic period.By 2025, the worldwide data science industry is expected to be worth more than USD 178 billion. Due to their comparable skill sets, the phrases data scientist and artificial intelligence engineer are frequently interchanged. They are not, however, interchangeable in terms of functionality.

Data science and artificial intelligence are attractive professional options in the technology field. Data science and artificial intelligence (AI) have been hailed as promising occupations in the tech industry. If you want to work in the tech industry, you might look into different data analysis parts to determine which one appeals to you the most.Lets take it a step further and talk about data science and AI careers without wasting any time. These are two of the most popular and sought-after technologies, each with its own set of concepts and uses. Well assist you with your decision-making process in this post. The distinctions between data science and artificial intelligence will be discussed. This will help you gain a better grasp of the two and decide which one to pursue as a career.

Artificial intelligence engineer

Artificial intelligence is no longer a distant memory but has become a more integral part of our daily lives. Artificial intelligence engineers are the one-man army imparting human intelligence to machines, from building a robot hand for solving Rubiks cube to speech recognition systems. AI is all around us, from getting groceries delivered to asking Alexa to play your favourite music. An artificial intelligence engineer is in charge of creating clever autonomous models and integrating them into the software.

An AI engineer helps construct models for AI-based applications using machine learning techniques such as neural networks. These engineers have technically created AI-based specific systems for different purposes like language translation, some picture identification, and sentiment-based contextual advertising, to name a few. They collaborate with business stakeholders to build AI solutions that can assist businesses to enhance operations, service delivery, and product creation.

Organizations are starting to see the full impact of AI and machine learning on their operations. Some organizations artificial intelligence engineers are more research-oriented, focusing on discovering the best model for a task while training, monitoring, and deploying AI systems in production. The majority of business analysts are upskilling and changing careers to become citizen data scientists. A company must be able to integrate AI into its applications to become completely AI-driven.

To ensure that business goals are matched with the analytics back end, AI developers engage with business analysts, data scientists, and architects. This allows everyone in the organization to obtain access to the information they need to make better decisions. More artificial intelligence engineer opportunities are being created for people who can handle data science, software development, and hybrid data engineering works.

Data Scientist

A data scientist is a unicorn who uses algorithms, math, statistics, design, engineering, communication, and management capabilities to somehow extract the particular meaningful, worthwhile and actionable insights from specific massive amounts of data and positively influence the organization. On the other hand, data scientists analyze databases and extract meaningful insights for future forecasts using technologies like big data analytics, cloud computing, and ML. To extract insights from data, data scientists employ statistical methodologies, distributed architecture, visualization tools, and various data-oriented technologies such as Hadoop, Spark, Python, SQL, and R.

Simply saying, data science is quite impossible and incomplete without AI. Data scientists use the information they collect to drive various business operations, analyze user metrics, identify potential business hazards, assess market trends, and make smarter decisions to achieve organizational goals. A data scientist uses machine learning and predictive analytics to cope with exceedingly vast and complex datasets.When it comes to establishing a successful business, both AI and data science have a unique role. The ability to design algorithms that enable the collection and cleaning of such a large amount of data and prepare it for analysis is crucial. As a result, if businesses want to compete with future employment, theyll need both AI and data science.

Responsibilities and roles

Engineer in Artificial Intelligence

An artificial intelligence engineer collaborates with the data science team to originate, develop, and deliver production-ready AI products for enhanced business processes. Fake intelligence developers are given various organizational duties in addition to developing techniques.

Theyre in charge of designing and developing computer vision solutions that take advantage of machine learning and deep learning. Convert artificial intelligence and machine learning models into application programme interfaces (APIs) to be used by other programmes. Using object tracking algorithms, instance segmentation, semantic, object detection, and keypoint detection create scalable algorithms. Assist stakeholders in comprehending the output. Machine learning techniques such as zero-shot learning, GANs, few-shot learning, and self-supervised procedures are used. Set up and manage AI product infrastructure and the automation of the data science teams infrastructure. To construct deployable versions of the model, we used Docker technologies. Conduct statistical analysis and interpretation to assist organizations in making data-driven decisions. Methods for testing and deployment Developing and deploying functional, clever AI algorithms.

Data Scientist

The majority of a data scientists day focuses on data. A prominent data engineer is responsible for the whole data management and processing systems design, development, construction, installation, testing, and maintenance. Data scientists may find themselves engulfed in these responsibilities, ranging from data collection to data analysis and transformation. Their primary responsibility is to locate raw data and make it accessible to other specialists.

They must identify business or engineering-related challenges, transform them into data science problems, locate sources, analyze data, and develop a solution. The company will be unable to collect data from diverse sources without the assistance of a prominent data engineer.

Integrate several programming languages and technologies to create a comprehensive solution. Using a variety of techniques and technologies, deliver end-to-end analytical solutions. To manage large amounts of data, create systems that are highly scalable, robust, and fault-tolerant. In-depth, hands-on expertise in research or corporate environment with machine learning, data mining, statistical modelling, and unstructured data analytics. To stay ahead of the competition, new big data management tools and technologies must be introduced. Demonstrating expertise in classification methods, neural networks, cluster analysis, Bayesian modelling, and stochastic modelling, among other topics. Examine alternative data collecting options and experiment with innovative ways to use existing data.

Data Scientist vs Artificial Intelligence Engineer

The primary and particular job of a Data Engineer is to design and engineer a reliable and good infrastructure for transforming specific data into such particular formats as can be used by Data Scientists. Apart from building scalable pipelines to convert semi-structured and unstructured data into usable forms, Data Engineers must also identify meaningful trends in large datasets. Companies offering these generous salaries recognize the power of big data and are eager to use it to boost business decisions.Essentially, Data Engineers likewise work to prepare and also make raw data more and more useful as well as worthy for analytical or operational uses. Salaries for data scientists and artificial intelligence engineers are quite heading skyward, and these vary based on representing skills, their experience level in the field, and the companies hiring as well. The average salary of a good and average data scientist is approximately $117,543 per year, to be guessed.However, due to the increasing demand and trending era for skilled data scientists and artificial intelligence engineers, the salaries for these specific professionals are always changing. What makes the job of artificial intelligence engineers is that they produce autonomous and intelligent models. Even starting salaries in this job line are looking so increasingly attractive in this day today growing field.

Deploying Artificial Intelligence solutions is their sole responsibility as well. They should also get to know about distributed computing as Artificial Intelligence engineers work with very large amounts of data that technically cannot be stored on a single machine.

As career opportunities for AI engineers rapidly expand, AI engineers salaries will continue to climb. Salary ranges from 30 lakhs per annum for a data scientist to 50 lakhs per annum for an artificial intelligence engineer, depending on seniority. For AI engineers, a better understanding of the human thought process is a must-have ability.

Which Profession Should You Pursue?

There is a long list of careers available that integrate data science and artificial intelligence. An artificial intelligence engineer helps businesses build novel products that bring autonomy, while a data scientist creates data products that foster profitable business decision making.

Jobs like AI data analysts, prominent data engineers require a combined knowledge of data science and artificial intelligence. AI engineers and data scientists work together closely to create usable products for clients.

The primary responsibility of an AI data analyst includes procuring, preparing, cleaning, and modelling data using machine learning models and new analytical methods. In addition, the AI data analyst is in charge of designing and developing data reports that will assist stakeholders in making better decisions. An AI data analysts compensation ranges from 2.5 to 7.3 lakh rupees around counted on average.

Prominent data engineers are skilled as software developers, and they have to be proficient in coding, excellent data scientist, and engineer all at the same time. Both data scientists and AI developers stay up to date on the latest innovative tools and technologies that have the potential to alter the customer experience, corporate operations, and the workforce.

This is a multi-faceted role, and any significant data engineer could find themselves performing a range of tasks on any day of the week. The average wages of a prominent and good data engineer range from 8 to 13 lakh rupees. However, a data scientist looks at the business more strategically than an artificial intelligence engineer.

Conclusion

We examined all of the nuances of the two subjects and how they are used interchangeably in this Data science vs AI blog. Artificial intelligence and extensive data engineering are witnessing a significant increase in demand in the employment market as we approach 2021.Data Science is concerned with the computations conducted on data, whereas AI is the technology that creates predictions based on the data. Both career fields are in very high demand in todays 21st industry. You are meant to work as a prominent data engineer if you are purely interested in data and extensive data management. However, before deciding between the two, you should confirm your interests and preferences.

The only thing you need to know is your chosen field. Artificial intelligence engineering is a better fit for you if you enjoy working with different teams and want to work with clustered data. If you enjoy data analysis, Data Science may be a good fit for you; but, if you enjoy the principles of AI and the enormous potential it contains, a job in the same sector may be a better fit for you.

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Job hunting nightmare: 1,000 plus job applications and still no offers – ABC Action News

ST. PETERSBURG, Fla. There have been plenty of news reports about labor shortages and businesses unable to fill positions throughout the pandemic. But, there is another side of this story that hasn't gotten enough attention; millions of people looking for jobs and can't get hired because of online algorithms, artificial intelligence, and more.

ABC Action News reporter Michael Paluska sat down with St. Petersburg resident Elizabeth Longden. She showed us all of the jobs she's applied for on LinkedIn and Indeed. More than a thousand applications were filed on LinkedIn and more than 140 on Indeed.

"So, business data strategy, talent and culture recruiter, diversity, equity and inclusion specialist, human resources," Longden said as she named off a few of the jobs she's applied for. "There are 128 pages with eight applications per page."

"That's a lot of jobs," Paluska said.

"Yeah, a lot," Longden replied with a half-smile that was more of an acknowledgment of her job woes.

"How do you process 1,000 plus rejections?" Paluska asked.

"It's discouraging, and fortunately, there haven't been 1,000 rejections. Most of the places don't even get back to you one way or the other," Longden said. "So yeah, we're looking at less than that. But it's still a big, you know, it's a big confidence blow, especially when you hear, oh, there's a labor crisis. And nobody wants to work. And like, hi, I would like to work."

According to the Bureau of Labor, a record 4.4 million people quit their jobs in September. That's a new all-time high. So, you would think millions of openings would help Longden. But, that's not the case.

Longden has a college degree, an insurance license, and a decade of work experience in human resources. In May, like many Americans throughout this pandemic, she was laid off from her company. So she took about a month off to reset and started the search in her field as an operations specialist, people ops, HR, and businesses operations.

"Have you ever been in a hole where you lost a job, and you couldn't get another one in the past?" Paluska asked.

"Not where I had lost one and couldn't get another one. I'd had times where I'd moved, you know, and had had trouble finding a job for maybe a month or two. But I was always able to find something," Longden said.

In September, the Harvard Business School released a study called Untapped Workers: Hidden Talent. The study explains this lack of hiring phenomenon. The lead author, Joseph Fuller, estimating millions of Americans are in the same position as Longden.

"So, you have this, this system that systematically excludes people that may not check every box in the employer's description of what they're looking for, but can be highly qualified on multiple parameters, even those the most important for job success, but they still get excluded," Fuller, professor of management practice at Harvard Business School said. "But what happens is, the employer in setting up these filters and ranking systems emphasizes some skills over others, intended to rely on two factors to make a decision."

The job search algorithms and artificial intelligence filter out candidates based on keywords before someone like Longden ever talks to a human being.

"And, the algorithms are unforgiving," Fuller said. "If you don't, if you don't have the right keywords, if you're just missing one of those attributes, you can get excluded from consideration even though you check every box on every other attribute they're looking for."

"Whose fault is that the company or LinkedIn or Indeed?" Paluska asked.

"You know, no company sets out to have a failed hiring process," Fuller said. "They provide the tools that their customers regularly ask for. So I think this is a tragedy, without a villain. It's the way companies have gone about it is optimized around minimizing the time it takes to find candidates in minimizing the cost of finding someone to hire. There's some kind of killer variable that is causing the system to say not qualified or not attractive relative to other applicants. The vast majority of those candidates never hear back anything just ghosted."

Longden has been ghosted a lot. One recruiter called her three times in a week asking for her to apply and when she thought she got the job, radio silence. Longden thought he was dead.

"I even was like, 'Are you alive?' You know, like, I just want to know, you're okay, you've just totally gone dark," Longden said.

Longden's job search hell has her skeptical of the entire process.

"I've also discovered that there's been a huge uptick in companies wanting pre-work from people. So all in all, I've probably done about 25 hours worth of pre-work for various companies, none of which has been compensated, and none of which I've even gotten a roll-out of," Longden said.

"Do you think they are using your work for their benefit?" Paluska asked.

"Oh, I'm sure," Longden said. "One of the things I was asked to create was an onboarding process for new employees. So that's what the role at the company would have been doing was onboarding their new employees as they came in. And so, one of the pre-work examples was to create an onboarding process from the offer to the 90-day mark of employment. And I did that. And I'm certain that they're having multiple people do that and pulling what they like best from everyone."

We reached out to LinkedIn and Indeed for comments but did not get a response back.

"Two or three quick suggestions for Elizabeth, the first is be very, very aware of language terms, and make your submission. Match what's being asked for, to the greatest degree you can with integrity," Fuller said. "The second thing I would say is, go on something like LinkedIn and look at the profiles of people who got the job you want. And what are they saying they do? What keywords are they using? Is there a regularly referenced tool that they claim expertise in that she doesn't have?"

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