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Narrow vs. General AI
Data Science

Narrow vs. General AI: What’s Next for Artificial Intelligence?

7 minute read | August 12, 2019
Leah Davidson

Written by:
Leah Davidson

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In 1950, Alan Turing asked, “Can machines think?”

At the time, it probably seemed like an outlandish suggestion, but fast-forward almost 70 years, and artificial intelligence can detect diseases, fly drones, translate between languages, recognize emotions, trade stocks, and even beat humans at “Jeopardy.” It seems like AI is indeed developing a mind of its own.

Artificial intelligence, a term coined by John McCarthy in 1956, began as a simulation of human intelligence through machines and computer systems.

Today, AI represents a way to process data and reach conclusions faster than humans, leading to more accurate predictions of the future. Google’s director of engineering, Ray Kurzeil, forecasts that machines will reach a human level of intelligence by 2029. Kurzeil also says that by 2045 we will reach technological singularity, a time when artificial intelligence becomes more powerful than humans. 

This inflection point will lead to a separation between AI as we know it today (also called “narrow AI”) and a future state of AI (“general AI”) that can apply intelligence to any problem.

What Is Narrow AI?

Narrow AI (ANI) is defined as “a specific type of artificial intelligence in which a technology outperforms humans in some very narrowly defined task. Unlike general artificial intelligence, narrow artificial intelligence focuses on a single subset of cognitive abilities and advances in that spectrum.” 

There are many examples of narrow AI around us every day, represented by devices like Alexa, Google Assistant, Siri, and Cortana. They include:

  • Self-driving cars
  • Facial recognition tools that tag you in pictures
  • Customer service bots that redirect inquiries on a webpage
  • Google’s page-ranking technology that determines which websites appear at the top of the search engine
  • Recommendation systems showing items that could be useful additions to your shopping cart based on browsing history
  • Spam filters that keep your inbox clean through automated sorting

Today, many companies are investing in and implementing ANI to improve efficiency, cut costs, and automate tasks; however, ANI has serious limitations. Here are some of the barriers to ANI:

  • ANI needs a large amount of high-quality data to yield accurate results, and not all environments meet these data requirements.
  • The learning curve to institutionalize AI properly can be steep. Companies have to set up and train their staff on new processes and technologies.
  • If a task changes, the effectiveness of an ANI system decreases, since it is programmed for a specific purpose.
  • Sometimes, replacing humans with rules-based machines leads to greater frustration and lowers customer satisfaction—for example, in the hospitality industry, where guests value personalized service and human interaction. 

As we address these obstacles and open up new use cases for AI, we are moving toward a new paradigm—that of general artificial intelligence.

What Is General AI?

Think of R2-D2 in “Star Wars” or Jarvis in “Iron Man” and you’ll get a sneak peek into what researchers are labeling as the future of artificial general intelligence (AGI). AGI recently received a $1 billion investment from Microsoft through OpenAI

But what exactly is AGI?

AGI, or “strong AI,” allows a machine to apply knowledge and skills in different contexts. This more closely mirrors human intelligence by providing opportunities for autonomous learning and problem-solving. 

The challenge now is to move from ANI to AGI in advanced fields like computer vision and natural language processing.

To reach AGI, computer hardware needs to increase in computational power to perform more total calculations per second (cps). Tianhe-2, a supercomputer created by China’s National University of Defense Technology, currently holds the record for cps at 33.86 petaflops (quadrillions of cps). Although that sounds impressive, the human brain is estimated to be capable of one exaflop (a billion billion cps). Technology still needs to catch up. 

Currently, one of the main approaches to AGI is called “whole brain emulation,” where a brain’s memory and mental state are transferred onto a computer. Computer architecture is similar to the brain’s because they can both operate through a system of neurons called neural networks. When the right action is taken, it strengthens the transistor connections in the firing pathways. Through trial and error, technology can learn and form smart neural pathways. 

To date, scientists have been able to replicate the brain of a 1-millimeter flatworm consisting of 302 neurons. The human brain, however, is estimated to contain 100 billion neurons, which means we have a ways to go before we can recreate our brain.

Quantum computers, which use quantum mechanics to process exponentially more data than normal computers, are positioned to be the next technological frontier to facilitate AGI.

For AGI to match human intelligence, it needs to be able to transfer learnings from one environment to another, use common sense, work collaboratively with other machine and human stakeholders, and attain consciousness. 

Neuroscientist Dr. Heather Berlin at the Icahn School of Medicine at Mount Sinai defined consciousness in three different ways: “pure subjective experience (‘Look, the sky is blue’), awareness of one’s own subjective experience (‘Oh, it’s me that’s seeing the blue sky’), and relating one subjective experience to another (‘The blue sky reminds me of a blue ocean’).” Developing artificial consciousness requires subjective, conscious experience in addition to pure intellectual horsepower.

Many experts have different predictions about when we will reach AGI. In May 2017, over 350 machine learning and neuroscience experts were surveyed and around 50% believed it would happen before 2060. Louis Rosenberg, CEO of the technology company Unanimous AI, predicts that it will happen sooner—around 2030and Patrick Winston, MIT professor and former director of the MIT Artificial Intelligence Laboratory, puts the date around 2040.

With all these forecasts, how will we know when we’ve reached AGI?

One of the most famous tests to compare the intelligence of humans and computers is the Turing test, where a human contrasts the conversational abilities of a human and machine. Apple co-founder Steve Wozniak also coined the “coffee test,” where a machine enters a typical home and figures out how to prepare a cup of coffee. Other tests evaluate whether robots can successfully attend college or replace important job functions with greater efficacy than human workers.

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Machine Learning and Deep Learning on the Road to AGI

So how does other AI terminology fit into the new model of ANI and AGI?

Machine learning describes the ability to find patterns and make decisions without instructions or pre-programming, the ability for computer systems to truly “learn” on their own. Machine learning therefore comprises a subset of AI, but not the other way around.

Deep learning is a subset of machine learning that “learns” from unsupervised and unstructured data that is processed through neural networks, algorithms with brain-like functions. 

Neural networks can develop through both training and inference. Training involves using different algorithms and improving on them over time while incorporating new data sources. Inference means that a machine can identify which data sources it needs to make a prediction through logical rules and deductive reasoning.

Research progress in machine learning and deep learning is facilitating the transition from ANI to AGI by enabling decision-making without explicit instructions.

Toward Artificial Super-Intelligence (ASI)

Artificial super-intelligence (ASI) is a step further from AGI, where artificial intelligence exceeds human capabilities to operate at a genius level. Since ASI is still hypothetical, there are no real limits to what ASI could accomplish, from building nanotechnology to producing objects to preventing aging.

Many philosophers and scientists have different theories about the feasibility of reaching ASI. Cognitive scientist David Chalmer believes that once AGI is achieved, it will be relatively straightforward to extend capabilities and efficiency to attain ASI. According to Moore’s law, computational power should double at least every two years, which suggests there may not be a limit to technology’s eventual power. 

One of the roadblocks to ASI is the complexity of global problems. Can machines really solve world hunger or stop climate change? Additionally, ASI will need an exceptional amount of data, even relative to AGI. Some believe that using genetic engineering to create a super-intelligent group of humans is the best bet at ASI, while others posit that ASI will involve a new generation of supercomputers.

The Future of AI

Although we still have a long way to go before AGI and ASI, AI is moving quickly, with new discoveries and milestones emerging all the time with the combination of data science. Relative to human intelligence, AI holds promise for being able to multitask, perfectly recall and memorize information, function continuously without breaks, make calculations at record speed, sift through lengthy records and documents, and make unbiased decisions with the assistance of programmers, data scientists, machine learning engineers, and deep learning researchers.

Recently, Google’s AlphaZero won a 100-game chess championship through reinforcement learning and IBM created robots that can provide formidable competition in world-class debate competitions.

As AI continues to take over more jobs, there are big debates over the ethics of AI and whether governments should step in to monitor and regulate growth. AI could transform human relationships, increase discrimination, invade personal privacy, pose security threats through autonomous weapons, and even, in some doomsday scenarios, end humanity as we know it.

These issues may sound daunting, but they also make the study of AI all the more intriguing and impactful. 

Companies are no longer just collecting data. They’re seeking to use it to outpace competitors, especially with the rise of AI and advanced analytics techniques. Between organizations and these techniques are the data scientists – the experts who crunch numbers and translate them into actionable strategies. The future, it seems, belongs to those who can decipher the story hidden within the data, making the role of data scientists more important than ever.

In this article, we’ll look at 13 careers in data science, analyzing the roles and responsibilities and how to land that specific job in the best way. Whether you’re more drawn out to the creative side or interested in the strategy planning part of data architecture, there’s a niche for you. 

Is Data Science A Good Career?

Yes. Besides being a field that comes with competitive salaries, the demand for data scientists continues to increase as they have an enormous impact on their organizations. It’s an interdisciplinary field that keeps the work varied and interesting.

10 Data Science Careers To Consider

Whether you want to change careers or land your first job in the field, here are 13 of the most lucrative data science careers to consider.

Data Scientist

Data scientists represent the foundation of the data science department. At the core of their role is the ability to analyze and interpret complex digital data, such as usage statistics, sales figures, logistics, or market research – all depending on the field they operate in.

They combine their computer science, statistics, and mathematics expertise to process and model data, then interpret the outcomes to create actionable plans for companies. 

General Requirements

A data scientist’s career starts with a solid mathematical foundation, whether it’s interpreting the results of an A/B test or optimizing a marketing campaign. Data scientists should have programming expertise (primarily in Python and R) and strong data manipulation skills. 

Although a university degree is not always required beyond their on-the-job experience, data scientists need a bunch of data science courses and certifications that demonstrate their expertise and willingness to learn.

Average Salary

The average salary of a data scientist in the US is $156,363 per year.

Data Analyst

A data analyst explores the nitty-gritty of data to uncover patterns, trends, and insights that are not always immediately apparent. They collect, process, and perform statistical analysis on large datasets and translate numbers and data to inform business decisions.

A typical day in their life can involve using tools like Excel or SQL and more advanced reporting tools like Power BI or Tableau to create dashboards and reports or visualize data for stakeholders. With that in mind, they have a unique skill set that allows them to act as a bridge between an organization’s technical and business sides.

General Requirements

To become a data analyst, you should have basic programming skills and proficiency in several data analysis tools. A lot of data analysts turn to specialized courses or data science bootcamps to acquire these skills. 

For example, Coursera offers courses like Google’s Data Analytics Professional Certificate or IBM’s Data Analyst Professional Certificate, which are well-regarded in the industry. A bachelor’s degree in fields like computer science, statistics, or economics is standard, but many data analysts also come from diverse backgrounds like business, finance, or even social sciences.

Average Salary

The average base salary of a data analyst is $76,892 per year.

Business Analyst

Business analysts often have an essential role in an organization, driving change and improvement. That’s because their main role is to understand business challenges and needs and translate them into solutions through data analysis, process improvement, or resource allocation. 

A typical day as a business analyst involves conducting market analysis, assessing business processes, or developing strategies to address areas of improvement. They use a variety of tools and methodologies, like SWOT analysis, to evaluate business models and their integration with technology.

General Requirements

Business analysts often have related degrees, such as BAs in Business Administration, Computer Science, or IT. Some roles might require or favor a master’s degree, especially in more complex industries or corporate environments.

Employers also value a business analyst’s knowledge of project management principles like Agile or Scrum and the ability to think critically and make well-informed decisions.

Average Salary

A business analyst can earn an average of $84,435 per year.

Database Administrator

The role of a database administrator is multifaceted. Their responsibilities include managing an organization’s database servers and application tools. 

A DBA manages, backs up, and secures the data, making sure the database is available to all the necessary users and is performing correctly. They are also responsible for setting up user accounts and regulating access to the database. DBAs need to stay updated with the latest trends in database management and seek ways to improve database performance and capacity. As such, they collaborate closely with IT and database programmers.

General Requirements

Becoming a database administrator typically requires a solid educational foundation, such as a BA degree in data science-related fields. Nonetheless, it’s not all about the degree because real-world skills matter a lot. Aspiring database administrators should learn database languages, with SQL being the key player. They should also get their hands dirty with popular database systems like Oracle and Microsoft SQL Server. 

Average Salary

Database administrators earn an average salary of $77,391 annually.

Data Engineer

Successful data engineers construct and maintain the infrastructure that allows the data to flow seamlessly. Besides understanding data ecosystems on the day-to-day, they build and oversee the pipelines that gather data from various sources so as to make data more accessible for those who need to analyze it (e.g., data analysts).

General Requirements

Data engineering is a role that demands not just technical expertise in tools like SQL, Python, and Hadoop but also a creative problem-solving approach to tackle the complex challenges of managing massive amounts of data efficiently. 

Usually, employers look for credentials like university degrees or advanced data science courses and bootcamps.

Average Salary

Data engineers earn a whooping average salary of $125,180 per year.

Database Architect

A database architect’s main responsibility involves designing the entire blueprint of a data management system, much like an architect who sketches the plan for a building. They lay down the groundwork for an efficient and scalable data infrastructure. 

Their day-to-day work is a fascinating mix of big-picture thinking and intricate detail management. They decide how to store, consume, integrate, and manage data by different business systems.

General Requirements

If you’re aiming to excel as a database architect but don’t necessarily want to pursue a degree, you could start honing your technical skills. Become proficient in database systems like MySQL or Oracle, and learn data modeling tools like ERwin. Don’t forget programming languages – SQL, Python, or Java. 

If you want to take it one step further, pursue a credential like the Certified Data Management Professional (CDMP) or the Data Science Bootcamp by Springboard.

Average Salary

Data architecture is a very lucrative career. A database architect can earn an average of $165,383 per year.

Machine Learning Engineer

A machine learning engineer experiments with various machine learning models and algorithms, fine-tuning them for specific tasks like image recognition, natural language processing, or predictive analytics. Machine learning engineers also collaborate closely with data scientists and analysts to understand the requirements and limitations of data and translate these insights into solutions. 

General Requirements

As a rule of thumb, machine learning engineers must be proficient in programming languages like Python or Java, and be familiar with machine learning frameworks like TensorFlow or PyTorch. To successfully pursue this career, you can either choose to undergo a degree or enroll in courses and follow a self-study approach.

Average Salary

Depending heavily on the company’s size, machine learning engineers can earn between $125K and $187K per year, one of the highest-paying AI careers.

Quantitative Analyst

Qualitative analysts are essential for financial institutions, where they apply mathematical and statistical methods to analyze financial markets and assess risks. They are the brains behind complex models that predict market trends, evaluate investment strategies, and assist in making informed financial decisions. 

They often deal with derivatives pricing, algorithmic trading, and risk management strategies, requiring a deep understanding of both finance and mathematics.

General Requirements

This data science role demands strong analytical skills, proficiency in mathematics and statistics, and a good grasp of financial theory. It always helps if you come from a finance-related background. 

Average Salary

A quantitative analyst earns an average of $173,307 per year.

Data Mining Specialist

A data mining specialist uses their statistics and machine learning expertise to reveal patterns and insights that can solve problems. They swift through huge amounts of data, applying algorithms and data mining techniques to identify correlations and anomalies. In addition to these, data mining specialists are also essential for organizations to predict future trends and behaviors.

General Requirements

If you want to land a career in data mining, you should possess a degree or have a solid background in computer science, statistics, or a related field. 

Average Salary

Data mining specialists earn $109,023 per year.

Data Visualisation Engineer

Data visualisation engineers specialize in transforming data into visually appealing graphical representations, much like a data storyteller. A big part of their day involves working with data analysts and business teams to understand the data’s context. 

General Requirements

Data visualization engineers need a strong foundation in data analysis and be proficient in programming languages often used in data visualization, such as JavaScript, Python, or R. A valuable addition to their already-existing experience is a bit of expertise in design principles to allow them to create visualizations.

Average Salary

The average annual pay of a data visualization engineer is $103,031.

Resources To Find Data Science Jobs

The key to finding a good data science job is knowing where to look without procrastinating. To make sure you leverage the right platforms, read on.

Job Boards

When hunting for data science jobs, both niche job boards and general ones can be treasure troves of opportunity. 

Niche boards are created specifically for data science and related fields, offering listings that cut through the noise of broader job markets. Meanwhile, general job boards can have hidden gems and opportunities.

Online Communities

Spend time on platforms like Slack, Discord, GitHub, or IndieHackers, as they are a space to share knowledge, collaborate on projects, and find job openings posted by community members.

Network And LinkedIn

Don’t forget about socials like LinkedIn or Twitter. The LinkedIn Jobs section, in particular, is a useful resource, offering a wide range of opportunities and the ability to directly reach out to hiring managers or apply for positions. Just make sure not to apply through the “Easy Apply” options, as you’ll be competing with thousands of applicants who bring nothing unique to the table.

FAQs about Data Science Careers

We answer your most frequently asked questions.

Do I Need A Degree For Data Science?

A degree is not a set-in-stone requirement to become a data scientist. It’s true many data scientists hold a BA’s or MA’s degree, but these just provide foundational knowledge. It’s up to you to pursue further education through courses or bootcamps or work on projects that enhance your expertise. What matters most is your ability to demonstrate proficiency in data science concepts and tools.

Does Data Science Need Coding?

Yes. Coding is essential for data manipulation and analysis, especially knowledge of programming languages like Python and R.

Is Data Science A Lot Of Math?

It depends on the career you want to pursue. Data science involves quite a lot of math, particularly in areas like statistics, probability, and linear algebra.

What Skills Do You Need To Land an Entry-Level Data Science Position?

To land an entry-level job in data science, you should be proficient in several areas. As mentioned above, knowledge of programming languages is essential, and you should also have a good understanding of statistical analysis and machine learning. Soft skills are equally valuable, so make sure you’re acing problem-solving, critical thinking, and effective communication.

Since you’re here…Are you interested in this career track? Investigate with our free guide to what a data professional actually does. When you’re ready to build a CV that will make hiring managers melt, join our Data Science Bootcamp which will help you land a job or your tuition back!

About Leah Davidson

A graduate of the Wharton School of Business, Leah is a social entrepreneur and strategist working at fast-growing technology companies. Her work focuses on innovative, technology-driven solutions to climate change, education, and economic development.