# data[].attributes.headline
TASK 1: A game of technological cat and mouse

# data[].attributes.subject
TASK 1: A game of technological cat and mouse

# data[].attributes.body
Deepfakes can cause untold harm to many - by being misleading, or offensive. Initiatives have been set up and are looking for ways to detect a deepfake video. The University of Albany published research showing that blinking irregularities could be used to sniff out a deepfake video. However, within months, the deepfake community (who have an open-source ethos and share their findings) managed to correct their software to stop the blinking irregularity. Thus creating a continual ‘cat & mouse’ effect.

# data[].attributes.body
In the analogue age of photography, images would be closely examined on film. Photographers would spend a lot of time with their images, considering composition, lighting adjustments and so on. In the digital age, that intimacy is lost. Images are scrolled through, quickly rated and discarded. Because the editing and post production processes have been replaced by software, the way we view our final images has also changed. Namely,  how rarely images see the light of day in any physical form. Photographs are made to be printed. Any photographer will tell you that a printed image has infinitely more life than a digital one. This could lead to a real disconnect between a photographer and their images, as its very rare any physical time is spend with an image.

# data[].attributes.body
Post production software is relying more and more on AI to make decisions as we edit our images.

# data[].attributes.body
How much someones thought process can change when they're forced to shoot with one roll of film as opposed to an unlimited amount of digital photos. How much more time and consideration is given to the production of image. Also with the knowledge that the manipulation potential is much lesser with analogue processes.

# data[].attributes.body
FILM
Ironic how the show talks about the dangers of AI and was recommended to me on Netflix through their AI deciding it would be a good match for me based on my viewing habits.
Talks about the dangers of AI in our daily lives, how it's influencing our decisions.

# data[].attributes.body
Face ID
Predictive text
What app to show us next
When do we wake up
What song to play

# data[].attributes.body
Duplex can replace the need for business' to answer their phone. Customers can talk to a very realistic sounding AI and can make reservations etc.

# data[].attributes.body
ARTICLE

# data[].attributes.attachment_link.readable_text
Reimagining our future working world

Right now, companies that are combining technologies like this can gain competitive advantages over more traditional rivals.

To take one example, a drone on its own is little more than a fancy radio-controlled helicopter - a toy. A drone with a camera is a surveillance tool. A drone with a camera, depth-sensors, a robotic arm, and AI is an autonomous tool that can radically change how we use warehouse space or deliver goods to customers by finding more efficient transport routes. Meanwhile, more traditional companies are still using slower, less efficient human-driven fork-lift trucks.

The next ten years will see an explosion of innovation as smart technologies mature and ever more businesses use them in combination with existing and emerging technologies to create radical new approaches to doing business and meet the ever-changing needs of their customers.

But remember this isn't about the technology itself, no matter how creatively it's combined. The pace of technological change will continue to accelerate - no one piece of technology will be enough to give a lasting competitive advantage in such an environment.

The truly successful businesses will be the ones that realize their transformation isn't a one-off change to adopt the latest technological tools, but an ongoing process - with the needs of people placed firmly as the focal point of innovative efforts, not the capabilities of the currently available tools.

Looking to 2030 and beyond, the impact of such human-focused innovation could radically change how the world works. The number of possible permutations is vast - and when AI enables these new combinatorial creations to continuously self-optimize, they should get continually more impactful.

It's time for us to reframe our relationship with technology, reimagining how to use technologies to meet real human needs, enable new ways of working, and power human enterprise. With the right approach, people can be both the drivers and beneficiaries of technological change, unlocking new paths to value and human engagement, helping us reinvent our organizations to be ready for what's next, and fully realizing human potential.

# data[].attributes.attachment_link.title
Is AI the start of the truly creative human?

# data[].attributes.attachment_link.description
Reimagining our future working world Right now, companies that are combining technologies like this can gain competitive advantages over more traditional rivals. To take one example, a drone on its own is little more than a fancy radio-controlled helicopter - a toy. A drone with a camera is a surveillance tool.

# data[].attributes.attachment_link.readable_text
None of these people exist. These images were generated using deepfake technology.
thispersondoesnotexist.com

Last month during ESPN's hit documentary series The Last Dance, State Farm debuted a TV commercial that has become one of the most widely discussed ads in recent memory. It appeared to show footage from 1998 of an ESPN analyst making shockingly accurate predictions about the year 2020.

As it turned out, the clip was not genuine: it was generated using cutting-edge AI. The commercial surprised, amused and delighted viewers.

What viewers should have felt, though, was deep concern.

The State Farm ad was a benign example of an important and dangerous new phenomenon in AI: deepfakes. Deepfake technology enables anyone with a computer and an Internet connection to create realistic-looking photos and videos of people saying and doing things that they did not actually say or do.

A combination of the phrases "deep learning" and "fake", deepfakes first emerged on the Internet in late 2017, powered by an innovative new deep learning method known as generative adversarial networks (GANs).

Several deepfake videos have gone viral recently, giving millions around the world their first taste of this new technology: President Obama using an expletive to describe President Trump, Mark Zuckerberg admitting that Facebook's true goal is to manipulate and exploit its users, Bill Hader morphing into Al Pacino on a late-night talk show.

The amount of deepfake content online is growing at a rapid rate. At the beginning of 2019 there were 7,964 deepfake videos online, according to a report from startup Deeptrace; just nine months later, that figure had jumped to 14,678. It has no doubt continued to balloon since then.

While impressive, today's deepfake technology is still not quite to parity with authentic video footage-by looking closely, it is typically possible to tell that a video is a deepfake. But the technology is improving at a breathtaking pace. Experts predict that deepfakes will be indistinguishable from real images before long.

"In January 2019, deep fakes were buggy and flickery," said Hany Farid, a UC Berkeley professor and deepfake expert. "Nine months later, I've never seen anything like how fast they're going. This is the tip of the iceberg."

Today we stand at an inflection point. In the months and years ahead, deepfakes threaten to grow from an Internet oddity to a widely destructive political and social force. Society needs to act now to prepare itself.

When Seeing Is Not Believing

The first use case to which deepfake technology has been widely applied- as is often the case with new technologies-is pornography. As of September 2019, 96% of deepfake videos online were pornographic, according to the Deeptrace report.

A handful of websites dedicated specifically to deepfake pornography have emerged, collectively garnering hundreds of millions of views over the past two years. Deepfake pornography is almost always non-consensual, involving the artificial synthesis of explicit videos that feature famous celebrities or personal contacts.

From these dark corners of the web, the use of deepfakes has begun to spread to the political sphere, where the potential for mayhem is even greater.

It does not require much imagination to grasp the harm that could be done if entire populations can be shown fabricated videos that they believe are real. Imagine deepfake footage of a politician engaging in bribery or sexual assault right before an election; or of U.S. soldiers committing atrocities against civilians overseas; or of President Trump declaring the launch of nuclear weapons against North Korea. In a world where even some uncertainty exists as to whether such clips are authentic, the consequences could be catastrophic.

Because of the technology's widespread accessibility, such footage could be created by anyone: state-sponsored actors, political groups, lone individuals.

In a recent report, The Brookings Institution grimly summed up the range of political and social dangers that deepfakes pose: "distorting democratic discourse; manipulating elections; eroding trust in institutions; weakening journalism; exacerbating social divisions; undermining public safety; and inflicting hard-to-repair damage on the reputation of prominent individuals, including elected officials and candidates for office."

Given the stakes, U.S. lawmakers have begun to pay attention.

"In the old days, if you wanted to threaten the United States, you needed 10 aircraft carriers, and nuclear weapons, and long-range missiles," U.S. Senator Marco Rubio said recently. "Today....all you need is the ability to produce a very realistic fake video that could undermine our elections, that could throw our country into tremendous crisis internally and weaken us deeply."

Technologists agree. In the words of Hani Farid, one of the world's leading experts on deepfakes: "If we can't believe the videos, the audios, the image, the information that is gleaned from around the world, that is a serious national security risk."

This risk is no longer just hypothetical: there are early examples of deepfakes influencing politics in the real world. Experts warn that these incidents are canaries in a coal mine.

Last month, a political group in Belgium released a deepfake video of the Belgian prime minister giving a speech that linked the COVID-19 outbreak to environmental damage and called for drastic action on climate change. At least some viewers believed the speech was real.

Even more insidiously, the mere possibility that a video could be a deepfake can stir confusion and facilitate political deception regardless of whether deepfake technology has actually been used. The most dramatic example of this comes from Gabon, a small country in central Africa.

In late 2018, Gabon's president Ali Bongo had not been seen in public for months. Rumors were swirling that he was no longer healthy enough for office or even that he had died. In an attempt to allay these concerns and reassert Bongo's leadership over the country, his administration announced that he would give a nationwide televised address on New Years Day.

In the video address (which is worth examining firsthand yourself), Bongo appears stiff and stilted, with unnatural speech and facial mannerisms. The video immediately inflamed suspicions that the government was concealing something from the public. Bongo's political opponents declared that the footage was a deepfake and that the president was incapacitated or dead. Rumors of a deepfake conspiracy spread quickly on social media.

The political situation in Gabon rapidly destabilized. Within a week, the military had launched a coup-the first in the country since 1964-citing the New Years video as proof that something was amiss with the president.

To this day experts cannot definitively say whether the New Years video was authentic, though most believe that it was. (The coup proved unsuccessful; Bongo has since appeared in public and remains in office today).

But whether the video was real is almost beside the point. The larger lesson is that the emergence of deepfakes will make it increasingly difficult for the public to distinguish between what is real and what is fake, a situation that political actors will inevitably exploit-with potentially devastating consequences.

"People are already using the fact that deepfakes exist to discredit genuine video evidence," said USC professor Hao Li. "Even though there's footage of you doing or saying something, you can say it was a deepfake and it's very hard to prove otherwise."

In two recent incidents, politicians in Malaysia and in Brazil have sought to evade the consequences of compromising video footage by claiming that the videos were deepfakes. In both cases, no one has been able to definitively establish otherwise-and public opinion has remained divided.

Researcher Aviv Ovadya warns of what she terms "reality apathy": "It's too much effort to figure out what's real and what's not, so you're more willing to just go with whatever your previous affiliations are."

In a world in which seeing is no longer believing, the ability for a large community to agree on what is true-much less to engage in constructive dialogue about it-suddenly seems precarious.

A Game of Technological Cat-And-Mouse

The core technology that makes deepfakes possible is a branch of deep learning known as generative adversarial networks (GANs). GANs were invented by Ian Goodfellow in 2014 during his PhD studies at the University of Montreal, one of the world's top AI research institutes.

In 2016, AI great Yann LeCun called GANs "the most interesting idea in the last ten years in machine learning."

Before the development of GANs, neural networks were adept at classifying existing content (for instance, understanding speech or recognizing faces) but not at creating new content. GANs gave neural networks the power not just to perceive, but to create.

Goodfellow's conceptual breakthrough was to architect GANs using two separate neural networks-one known as the "generator", the other known as the "discriminator"-and pit them against one another.

Starting with a given dataset (say, a collection of photos of human faces), the generator begins generating new images that, in terms of pixels, are mathematically similar to the existing images. Meanwhile, the discriminator is fed photos without being told whether they are from the original dataset or from the generator's output; its task is to identify which photos have been synthetically generated.

As the two networks iteratively work against one another-the generator trying to fool the discriminator, the discriminator trying to suss out the generator's creations-they hone one another's capabilities. Eventually the discriminator's classification success rate falls to 50%, no better than random guessing, meaning that the synthetically generated photos have become indistinguishable from the originals.

One reason deepfakes have proliferated is the machine learning community's open-source ethos: starting with Goodfellow's original paper, whenever a research advance in generative modeling occurs, the technology is generally made available for free for anyone in the world to download and make use of.

Given that deepfakes are based on AI in the first place, some look to AI as a solution to harmful deepfake applications. For instance, researchers have built sophisticated deepfake detection systems that assess lighting, shadows, facial movements, and other features in order to flag images that are fabricated. Another innovative defensive approach is to add a filter to an image file that makes it impossible to use that image to generate a deepfake.

A handful of startups have emerged that offer software to defend against deepfakes, including Truepic and Deeptrace.

Yet such technological solutions are not likely to stem the spread of deepfakes over the long term. At best they will lead to an endless cat-and-mouse dynamic, similar to what exists in cybersecurity today, in which breakthroughs on the deepfake detection side spur further innovation in deepfake generation. The open-source nature of AI research makes this all the more likely.

To give one example, in 2018 researchers at the University of Albany published analysis showing that blinking irregularities were often a telltale sign that a video was fake. It was a helpful breakthrough in the fight against deepfakes-until, within months, new deepfake videos began to emerge that corrected for this blinking imperfection.

"We are outgunned," said Farid. "The number of people working on the video-synthesis side, as opposed to the detector side, is 100 to 1."

The Path Forward

Looking beyond purely technological remedies, what legislative, political, and social steps can we take to defend against deepfakes' dangers?

One tempting, simple solution is to pass laws that make it illegal to create or spread deepfakes. The state of California has experimented with this approach, enacting a law last year that makes it illegal to create or distribute deepfakes of politicians within 60 days of an election. But a blanket deepfake ban has both constitutional and practical challenges.

The First Amendment of the U.S. Constitution enshrines the freedom of expression. Any law proscribing online content, particularly political content, risks running afoul of these constitutional protections.

"Political speech enjoys the highest level of protection under U.S. law," said law professor Jane Kirtley. "The desire to protect people from deceptive content in the run-up to an election is very strong and very understandable, but I am skeptical about whether they are going to be able to enforce [the California] law."

Beyond constitutional concerns, deepfake bans will likely prove impracticable to enforce due to the anonymity and borderlessness of the Internet.

Other existing legal frameworks that might be deployed to combat deepfakes include copyright, defamation and the right of publicity. But given the broad applicability of the fair use doctrine, the usefulness of these legal avenues may be limited.

In the short term, the most effective solution may come from major tech platforms like Facebook, Google and Twitter voluntarily taking more rigorous action to limit the spread of harmful deepfakes.

Relying on private companies to solve broad political and societal problems understandably makes many deeply uncomfortable. Yet as legal scholars Bobby Chesney and Danielle Citron put it, these tech platforms' terms-of-service agreements are "the single most important documents governing digital speech in today's world." As a result, these companies' content policies may be "the most salient response mechanism of all" to deepfakes.

A related legislative option is to amend the controversial Section 230 of the Communications Decency Act. Written in the early days of the commercial Internet, Section 230 gives Internet companies almost complete civil immunity for any content posted on their platforms by third parties. Walking these protections back would make companies like Facebook legally responsible for limiting the spread of damaging content on their sites. But such an approach raises complex free speech and censorship concerns.

In the end, no single solution will suffice. An essential first step is simply to increase public awareness of the possibilities and dangers of deepfakes. An informed citizenry is a crucial defense against widespread misinformation.

The recent rise of fake news has led to fears that we are entering a " post-truth " world. Deepfakes threaten to intensify and accelerate this trajectory. The next major chapter in this drama is likely just around the corner: the 2020 elections. The stakes could hardly be higher.

"The man in front of the tank at Tiananmen Square moved the world," said NYU professor Nasir Memon. "Nixon on the phone cost him his presidency. Images of horror from concentration camps finally moved us into action. If the notion of not believing what you see is under attack, that is a huge problem. One has to restore truth in seeing again."

# data[].attributes.attachment_link.title
Deepfakes Are Going To Wreak Havoc On Society. We Are Not Prepared.

# data[].attributes.attachment_link.description
Last month during ESPN's hit documentary series The Last Dance, State Farm debuted a TV commercial that has become one of the most widely discussed ads in recent memory. It appeared to show footage from 1998 of an ESPN analyst making shockingly accurate predictions about the year 2020.

# data[].attributes.body
ARTICLE

# data[].attributes.attachment_link.readable_text
It seems weird to even use these two terms in a sentence together and now we are on a path where Artificial Intelligence will help to augment the Human Creativity.

The Human Creativity is a trait that is characterized by human beings but now with all these advancements in the field of technology, the future is leaning towards A.I. The term creativity can be defined as a fundamental feature of human intelligence that has always been a challenge for Artificial Intelligence.

But in the year 2018, Artificial Intelligence trends are proceeding towards overcoming this hurdle of Human Creativity. There are various AI techniques that can be used to give birth to new ideas and some of them are mentioned below:

By deeply exploring the actual potential of conceptual spaces.

By making further transformations that can enable the generation of ideas that were previously impossible.

By producing revolutionary combinations of familiar ideas.

What makes AI different than others

Machine Learning, Artificial Learning, and Deep Learning, these three technologies are misunderstood and also misinterpreted by a majority of people. Some even end up using these terms interchangeably, that's why we need to first know the basic meaning of each of these technologies:

Machine Learning

Machine Learning also known as ML is actually a subfield of Artificial Intelligence, that is entirely devoted in creating programs to increase the efficiency when more information is fed to the machine. In ML, there is no need for the developer to continuously add programs for this improvement.

Deep Learning

The term Deep Learning (DL) can be defined as a set of Machine Learning techniques that are based on loose models on how the brain neurons communicate with other neurons and then adapt to new information introduced.

Artificial Intelligence

We can define AI as a field of Computer Science that basically teaches about how to build computers by relating it to new types of tasks. These tasks are particularly those tasks that can not be performed by an average human being.

Machine Learning, A.I., and Deep Learning are the types of technologies that can easily perform tasks in a few seconds and those same tasks when performed by humans can take a couple of hours.

Link between AI and Human Creativity

The history of AI is filled with mixed reviews, for example - Founder of Tesla, Elon Musk got into a heated Twitter rant with Facebook's founder Mark Zuckerberg calling AI as a great fundamental risk to the existence of mankind.

But there are many researchers and entrepreneurs who are firm believers of the fact that there are indeed many significant uses of Artificial Intelligence. Here are some of the expectations from the link that they are trying to establish between AI and Human creativity:

1. When human intelligence is combined with the principles of Big Data and AI, we will get answers to questions like 'cancer cure', 'reverse climate' and much more.

2. Prescriptive Intelligence to be better than Predictive Intelligence in the field of Healthcare.

3. People will be able to gain trust in AI assistants when they will educate themselves on the components of AI.

4. Using explainable and interpretable models are important for providing a better understanding of the concepts.

5. After seeing small victories achieved with AI in various different fields, people will gain more faith in its operation.

6. With the automation feature of AI, our dependencies from some tiresome tasks have been removed so that we can now focus more on creative and visionary ideas.

What comes to your mind when you think of being creative? You think of creating some kind of Art, right!
Humans create art because they want to become the creator of a beautiful masterpiece whereas the AI's contribution in the art will be first because of its programming functions. So, here are some of the those AI artists that have managed to create a form of art:

A.I. can Rap like Kanye

With an open-source and 6,000 lines of Kanye West' song/rap lyrics, a high-school student was able to make his AI a new-age rapper. Initially, the AI could only rearrange the lines of West's music but after more optimization in the code, it can not write new words by itself any create song lyrics and then rap it too.

A.I. Horror Writer named Shelley

Shelley is the name given to a neural network that curates horror stories. This A.I. horror writer first came into limelight at the end of 2017 when some of the Shelley work was posted online on Twitter. This neural network creates chilling tales of horror all by itself after its creator fed hundreds of thousands of horror stories to it.

A.I. to help you paint like true Masterpiece

Now you can learn to paint like famous artists with the A.I. called 'Vincent' which embeds fine-art masters in order to assist the human artists. Vincent produces the digital layouts of some of the widely known art pieces and paintings of the 19th century. Another feature of what makes Vincent unique is that it can also submit a user's painting and guide them in enhancing it with a known artist's technique.

A.I. is here to support the Creative Process

With A.I. you can collect meaningful information with in-depth insights that will help to iterate basic ideas into a coordinated creative direction. This complete process will guide the way towards exploring new possibilities in various fields.

A.I. is not here to take over jobs and replace manpower but A.I. is here to assist the humans in order to create a better outcome that is more effective as well as more efficient.

Follow

# data[].attributes.attachment_link.title
Will AI Put A Limit On Human Creativity?

# data[].attributes.attachment_link.description
It seems weird to even use these two terms in a sentence together and now we are on a path where Artificial Intelligence will help to augment the Human Creativity. The Human Creativity is a trait that is characterized by human beings but now with all these advancements in the field of technology, the future is leaning towards A.I.

# data[].attributes.body
ARTICLE

# data[].attributes.attachment_link.readable_text
The rise of artificial intelligence (AI) has been met with mixed reviews. Many esteemed technology enthusiasts have sounded blaring alarm bells. The late Stephen Hawking declared that AI could be the "worst event in the history of our civilization". Elon Musk has reverberated similar sentiments--"AI is a fundamental risk to the existence of human civilization."

It's hard to dispute the inevitable imminent slaughter of jobs, especially those that involve rote work and highly repetitive actions. A recent by McKinsey predicted that AI will replace 75-375 million jobs (3-14% of the global workforce) by 2030. The question of whether or not an "AI apocalypse" threatens human creativity, however, is much more contentious. Indeed, AI technologies have already made an impact on creative disciplines. IBM's Watson, for example, has already produced an AI-generated sci-fi movie trailer for 20th Century Fox, discovered and invented cooking recipes, and created thousands of ads for Toyota.

Yet, while AI technologies can exhibit some creative prowess, they are unable to replace higher-order levels of creativity. A recent PwC sheds light on this reality. Most US consumers and business decision makers are confident that AI will eventually be able to achieve the following creative accomplishments:

Make a top 100 Billboard song

Create a piece of art that is worth over $100,000

Write a hit TV series.

However, consumers and business decision makers are less optimistic about AI's potential to create truly top-notch award-winning work. They are least optimistic that AI will be able to accomplish the following:

Author a New York Times bestseller

Write a movie that wins an Oscar

Win a Pulitzer Prize for journalism

For the time being, it seems top-drawer creativity is a talent reserved for humans. Rather than threatening to supersede the most creative human pursuits, AI will augment our creative capabilities.
Incentivizing creativity
When it comes to raw intelligence, AI is a great equalizer. No longer do individuals require a high-level quantitative academic degree to access, analyze, and digest huge volumes of data instantly. The competitive advantage that's historically been tied to high IQ levels has decreased in importance as AI has, and will continue to, level the intellectual playing field.

As the relative importance of IQ has declined, creativity and imagination have gained value and esteem. A 2016 World Economic Forum concluded that creativity will be the 3rd most important skill in the workplace on 2020, vaulting in importance from 10th position in 2015. Creativity is clearly the skill of the future .

As we enter the age of AI, our great distinguishing ability will be creativity. A explains, "The rise of artificial intelligence is driving a new shift in value creation focused on sentiments more intrinsic to the human experience: thinking, creativity and problem-solving." Creative expert and anthropologist Dr. Michael Bloomfield envisions tomorrow's new reality, "The typical CV or resume just five years from now might look very different. Your experience and competence will count for a whole lot less if a computer program that's just been released can do most of your work at a fraction of the cost...You might want to say where you got your creativity training, what level you reached and what creative ideas you've had as a result - forget waffling on about being able to think outside the box, you'll have to actually show some evidence of it. Maybe you'll even have a 'creative IQ' score to point to - hopefully one to boast about."

As increased value is placed on creativity and the trait becomes our best defense against obsolescence by AI, the workplace will increasingly reward creative valor. As we're incentivized and rewarded for creativity, it's inevitable that we will double down on our efforts to enhance our own imaginative and original thought processes. Given that research has shown that creativity can be learned, it's only a matter of time before we see soaring levels of creativity transpire.
Creating an environment that optimizes creative output
One of creativity's worst enemies is ourselves. When our cognitive load--the amount of information in our working memory--is overwhelmed, our creative aptitude is impaired. A published in Psychological Science found that individuals who exhibit the highest cognitive loads are least creative, whereas individuals with the lowest cognitive loads are most creative. When our mind is too encumbered by mental taxation, we're less likely to seek out novelty. Creativity cannot be accomplished by brute force; it requires a specific breeding ground.

AI makes for an environment that fuels creativity. With the capabilities to handle the repetitive and deterministic activities that consume the majority of our current days, AI mentally liberates us to engage in creative pursuits. Scientific has revealed that the parts of our brain responsible for creativity are only activated when the frequently used parts of our brains are deactivated.

Consider the fact that sales reps can spend of their time qualifying leads and only 20% closing deals. AI technologies are able to qualify leads for sales reps, enabling sales reps to divert more of their focus to closing deals with customers. Their creative engines begin to buzz as a result. The best sales pitches are creative ones where sales reps differentiate themselves from competitors and leave a lasting impression. AI assistants will schedule our appointments for us, surface only the emails that we need to respond to, and prioritize our tasks. We'll be less likely to thumb out text messages while in meetings and engage in other acts of multitasking that deplete the oxygenated glucose levels in our brain. In effect, our potential for creativity will soar to new heights.
Increasing our creative output
When it comes to AI, we've reached a critical inflection point. In years past, we've built advanced analytics frameworks to analyze large volumes of data more quickly and more accurately than can be achieved by humans. We've explicitly programmed machines to perform select tasks. In doing so, we've gained predictive power to forecast future outcomes. We now have answers to key questions. Which customers are most likely to purchase my product? How much revenue can I expect to gain from this customer? Which product is this customer most likely to purchase next?

While predictive power is valuable, it leaves little room for creative thought. We're told the most likely outcomes and are forced to react in a largely calculative manner. We pursue the customers who exhibit the highest buying propensities. We focus on the customers most likely to generate the most revenue. And we recommend specific products to customers, often without sufficient justification. With AI advancements, we'll be able to shift from predictive to prescriptive intelligence. Because AI is able to simulate and learn from an infinite body of potential outcomes, it gives more context to the predictive recommendations we've blindly followed in the past. It helps us answer the more difficult question: "why?". Why is this customer most likely to purchase my product? Why is customer X worth more than customer Y? Why is this tier of products best suited for this customer?

Armed with an understanding of the "why" behind the data, the sky's the limit. We're empowered to engage in creative thought. Because we have much more insight, a lot of guesswork is eliminated. Instead of merely telling customers what they should do, a sales rep is able to explain why it's in the customer's best interest to do it. We're given more insight to creatively explore which sales script will resonate most strongly with each customer and which marketing messaging will drive the greatest levels of engagement. Perhaps most important, we're able to deeply personalize sales and marketing content, an endeavor that only of marketing professionals believe they're effective at (despite 74% believing it has a strong impact on advancing customer relationships). Prescriptive intelligence ignites our creativity and imagination. It's ours for the taking.

While AI has enormous potential to help us reach our creative potential, of people believe they are not living up to their creative potential. In order to most effectively future-proof our brain from AI, we must fixate on enhancing our creative capacities and abilities . We've been focusing on whether AI will overtake humans. Instead, we ought to focus on determining how to work together with AI in such a way that a way that the whole is greater than the sum of the individual parts. With the right perspective, AI will amplify and augment our creative potential and ensure that Bill Gates is correct in declaring, AI will make our lives "more productive and creative'".

... Read More

# data[].attributes.attachment_link.description
The rise of artificial intelligence (AI) has been met with mixed reviews. Many esteemed technology enthusiasts have sounded blaring alarm bells. The late Stephen Hawking declared that AI could be the "worst event in the history of our civilization". Elon Musk has reverberated similar sentiments--"AI is a fundamental risk to the existence of human civilization."

# data[].attributes.headline
A report into how Ai and Man are becoming more similar 

# data[].attributes.subject
A report into how Ai and Man are becoming more similar 

# data[].attributes.headline
How Ai impacts photography specifically 

# data[].attributes.subject
How Ai impacts photography specifically 

# data[].attributes.attachment_link.readable_text
If you're wondering how good your next phone's camera is going to be, it'd be wise to pay attention to what the manufacturer has to say about AI. Beyond the hype and bluster, the technology has enabled staggering advances in photography over the past couple of years, and there's no reason to think that progress will slow down.

There are still a lot of gimmicks around, to be sure. But the most impressive recent advancements in photography have taken place at the software and silicon level rather than the sensor or lens - and that's largely thanks to AI giving cameras a better understanding of what they're looking at.

Google Photos provided a clear demonstration of how powerful a mix AI and photography would be when the app launched in 2015. Prior to then, the search giant had been using machine learning to categorize images in Google+ for years, but the launch of its Photos app included consumer-facing AI features that would have been unimaginable to most. Users' disorganized libraries of thousands of untagged photos were transformed into searchable databases overnight.

Suddenly, or so it seemed, Google knew what your cat looked like.

Google built on the previous work of a 2013 acquisition, DNNresearch, by setting up a deep neural network trained on data that had been labeled by humans. This is called supervised learning; the process involves training the network on millions of images so that it can look for visual clues at the pixel level to help identify the category. Over time, the algorithm gets better and better at recognizing, say, a panda, because it contains the patterns used to correctly identify pandas in the past. It learns where the black fur and white fur tend to be in relation to one another, and how it differs from the hide of a Holstein cow, for example. With further training, it becomes possible to search for more abstract terms such as "animal" or "breakfast," which may not have common visual indicators but are still immediately obvious to humans.

It takes a lot of time and processing power to train an algorithm like this, but after the data centers have done their thing, it can be run on low-powered mobile devices without much trouble. The heavy-lifting work has already been done, so once your photos are uploaded to the cloud, Google can use its model to analyze and label the whole library. About a year after Google Photos was launched, Apple announced a photo search feature that was similarly trained on a neural network, but as part of the company's commitment to privacy the actual categorization is performed on each device's processor separately without sending the data. This usually takes a day or two and happens in the background following setup.

Intelligent photo management software is one thing, but AI and machine learning are arguably having a bigger impact on how images are captured in the first place. Yes, lenses continue to get a little faster and sensors can always get a little bigger, but we're already pushing at the limitations of physics when it comes to cramming optical systems into slim mobile devices. Nevertheless, it's not uncommon these days for phones to take better photos in some situations than a lot of dedicated camera gear, at least before post-processing. That's because traditional cameras can't compete on another category of hardware that's just as profound for photography: the systems-on-chip that contain a CPU, an image signal processor, and, increasingly, a neural processing unit (NPU).

This is the hardware leveraged in what's come to be known as computational photography, a broad term that covers everything from the fake depth-of-field effects in phones' portrait modes to the algorithms that help drive the Google Pixel's incredible image quality. Not all computational photography involves AI, but AI is certainly a major component of it.

Apple makes use of this tech to drive its dual-camera phones' portrait mode. The iPhone's image signal processor uses machine learning techniques to recognize people with one camera, while the second camera creates a depth map to help isolate the subject and blur the background. The ability to recognize people through machine learning wasn't new when this feature debuted in 2016, as it's what photo organization software was already doing. But to manage it in real time at the speed required for a smartphone camera was a breakthrough.

Google remains the obvious leader in this field, however, with the superb results produced by all three generations of Pixel as the most compelling evidence. HDR+, the default shooting mode, uses a complex algorithm that merges several underexposed frames into one and, as Google's computational photography lead Marc Levoy has noted to The Verge, machine learning means the system only gets better with time. Google has trained its AI on a huge dataset of labeled photos, as with the Google Photos software, and this further aids the camera with exposure. The Pixel 2, in particular, produced such an impressive level of baseline image quality that some of us at The Verge have been more than comfortable using it for professional work on this site.

Google's Night Sight is a stunning advertisement for the role of software in photography

But Google's advantage has never seemed so stark as it did a couple of months ago with the launch of Night Sight. The new Pixel feature stitches long exposures together and uses a machine learning algorithm to calculate more accurate white balance and colors, with frankly astonishing results. The feature works best on the Pixel 3, because the algorithms were designed with the most recent hardware in mind, but Google made it available for all Pixel phones - even the original, which lacks optical image stabilization - and it's a stunning advertisement for how software is now more important than camera hardware when it comes to mobile photography.

That said, there is still room for hardware to make a difference, particularly when it's backed by AI. Honor's new View 20 phone, along with parent company Huawei's Nova 4, are the first to use the Sony IMX586 image sensor. It's a larger sensor than most competitors and, at 48 megapixels, represents the highest resolution yet seen on any phone. But that still means cramming a lot of tiny pixels into a tiny space, which tends to be problematic for image quality. In my View 20 tests, however, Honor's "AI Ultra Clarity" mode excels at making the most of the resolution, descrambling the sensor's unusual color filter to unlock extra detail. This results in huge photographs that you can zoom into for days.

Image signal processors have been important to phone camera performance for a while, but it looks likely that NPUs will take on a larger role as computational photography advances. Huawei was the first company to announce a system-on-chip with dedicated AI hardware, the Kirin 970, although Apple's A11 Bionic ended up reaching consumers first. Qualcomm, the biggest supplier of Android processors worldwide, hasn't made machine learning a major focus yet, but Google has developed its own chip called the Pixel Visual Core to help with AI-related imaging tasks. The latest Apple A12 Bionic, meanwhile, has an eight-core neural engine that can run tasks in Core ML, Apple's machine learning framework, up to nine times faster than the A11, and for the first time it's directly linked to the image processor. Apple says this gives the camera a better understanding of the focal plane, for example, helping generate more realistic depth of field.

The camera is an essential feature of any phone, and AI is our best shot at improving it

This kind of hardware will be increasingly important for efficient and performant on-device machine learning, which has an exceptionally high ceiling in terms of its demands on the processor. Remember, the kind of algorithms that power Google Photos were trained on huge, powerful computers with beefy GPUs and tensor cores before being set loose on your photo library. Much of that work can be done "in advance," so to speak, but the ability to carry out machine learning calculations on a mobile device in real time remains cutting edge.

Google has shown some impressive work that could reduce the processing burden, while neural engines are getting faster by the year. But even at this early stage of computational photography, there are real benefits to be found from phone cameras that have been designed around machine learning. In fact, out of all the possibilities and applications raised by the AI hype wave of the past few years, the area with the most practical use today is arguably photography. The camera is an essential feature of any phone, and AI is our best shot at improving it.

# data[].attributes.attachment_link.description
If you're wondering how good your next phone's camera is going to be, it'd be wise to pay attention to what the manufacturer has to say about AI. Beyond the hype and bluster, the technology has enabled staggering advances in photography over the past couple of years, and there's no reason to think that progress will slow down.

# data[].attributes.attachment_link.readable_text
If you weren't aware of the strong connection between AI and photography, you're not alone. Nevertheless, in the past decade, AI has contributed sizeable advances in the field of digital photography and is slated to continue revolutionizing the way we take photos. Thanks to computational photography and supervised machine learning algorithms, simple smartphone cameras can now perform advanced digital image enhancements, such as digital zoom, HDR merging, and digital refocusing.

Wait a minute, what is computational photography?

According to Marc Levoy, a pioneer in the field, computational photography can be explained as a variety of "computational imaging techniques that enhance or extend the capabilities of digital photography output." Beyond what camera hardware can do alone, computational photography employs software algorithms to alter or enhance photographic images.

How does AI enhance digital photography?

According to popular belief, digital photography is largely a function of the quality of the hardware one is working with - the camera lens and sensors. Those camera parts have indeed developed considerably in the past decade, and now offer unprecedented quality and speed.

Almost uniformly across manufacturers, specs
have improved so much that it doesn't really matter which brand of hardware you
choose today - you're guaranteed to get a superb quality of photographs either
way. What is starting to make a tangible difference, however, is the software
embedded in digital cameras, including those in smartphone cameras. Thus,
despite the similar hardware in the two types of devices, there are sizeable
differences in the quality of Google Pixel phone photos and those taken with
the latest iPhone model.

The upside of artificially enhanced photos

To begin with, computational photography has extended the capabilities of digital cameras to unprecedented heights. On top of those advances, some hardware manufacturers have also invested in incorporating AI into their camera software. Google, for instance, has registered notable progress with the AI embedded in the Google Camera app, as well as the development of its own AI-powered microchip, now included in all Google Pixel phones starting with the Pixel 2.

For a while now, Google has also used the
photos that users upload in the (now obsolete) Google+ social network, and in
the Google Photos app, to train its AI algorithms to recognize, categorize and
group images, improve image quality, and add extra features (e.g. the blurred
background effect). These features do not only help users up their photography
game, but also further the development of a superior artificial intelligence, capable
of analyzing and enhancing photos in ways previously unthinkable.

AI-enabled camera apps

To take reasonably good photos, we had to lug around heavy cameras, attach a different lens for each type of photo, and play with tons of settings to capture the most optimal light. AI-enabled phone camera apps have now made professional photography equipment nearly obsolete. While specific camera lenses may still be required for certain shots or environments, most amateur photographers find that phone cameras are not only more convenient to use, but also provide nearly the same quality of output - or sometimes even better - depending on the setting.

This result is largely due to a game-changing element that smartphone cameras are now packing: a neural processing unit (NPU). When it comes to impact on quality, the NPU is equally important as some of the more traditional parts that make up a camera - the image processor and the CPU. The NPU will become even more central to machine learning and AI going forward, as they require a fast and efficient processing power to function properly, especially on end user mobile devices.

Business applications of computational photography and AI

Now that AI can analyze and enhance photographs, different algorithms can be employed to achieve targeted results. Algorithms that reconstruct depth, refocus, adjust viewpoints, or merge HDR can turn simple digital lenses into complex equipment that not only captures but analyses and interprets inputs to provide sophisticated insights.

As neural processing units become more advanced, phone cameras powered by machine learning can enable other applications that we haven't yet thought of, for instance in health and wellness. A simple example of such a use case is the mobile app Welltory, which uses the smartphone camera to record videos of one's fingertips, which are then used to measure HRV (heart rate variability) - a predictor of general health.

Additional computational photography features, such as these, can come in handy in different sectors and applications:

Shadow detection and correction can be used in surveillance technology

Image reconstruction can be achieved for badly damaged or aged documents

Automatic facial and body photographic enhancements have proven popular with consumers.

As computational power and
algorithms in photography are perfected, the use cases for this technology will
become virtually limitless, thus opening the gates for additional B2B and B2C mobile
applications that have the potential to disrupt traditional processes and ways
of working.
Ready to brainstorm on how to take advantage of computational photography in your business? Contact the Pegus Digital team for a no-obligation consultation.
Copywriter inadanova.com

# data[].attributes.attachment_link.title
AI Disrupting Digital Photography - Pegus Digital

# data[].attributes.attachment_link.description
If you weren't aware of the strong connection between AI and photography, you're not alone. Nevertheless, in the past decade, AI has contributed sizeable advances in the field of digital photography and is slated to continue revolutionizing the way we take photos.

# data[].attributes.attachment_link.description
In recent years, artificial intelligence has revolutionized the photo industry. Neutral networks and machine learning have made complex editing actions available with a few clicks, and these days, brands can…