Gemma Milne - Author, Technology Writer

Gemma Milne isnt messing around. The world is in trouble and not just because of COVID-19. Hype and fraud fascinates her and Artificial Intelligence is one of those areas rife in both. Milne is on a mission to get everyone talking about it...properly.

Smoke + Mirrors is Milnes first book and focuses on the misuse of technical terminology. You might not think incorrectly using terms like AI is a big deal in the grand scheme of things but youd be wrong. Influence is everywhere and there is big money involved; ...it influences flow of funding, policy-making, voting, consumer behaviour, all sorts. Advertising agencies spend a great deal of time and money telling clients how important it is to sway people with narratives - their business makes no sense if words have no impact, right? says Milne.

While bluster around Blockchains ability to save the world didnt make it in the book, AI did as Milne believes the area has the biggest potential to be harmful if technology terminology is misused; There's such a cult of entrepreneurship around [AI] and a severe lack of reality in its general coverage. There are way too many people funding words on a slide deck, far too many people having philosophical discussions around the singularity as opposed to holding those in power right now to account, and far too many people still super influenced with the sci-fi narratives which have been in popular media over the last few decades.

Impact-wise, Milne believes it is tough to quantify and more funding is needed to wage a war on hype akin to fake news. Milne is clear in her intent; The point of the book is to arm each and every one of us with the insight, tools and understanding of how hype works, so we can better manage what information does to us and - ultimately - create better futures for us all. Smoke and Mirrors is there to help you fight a future we dont want to happen.

The result of doing nothing could be catastrophic for humanity. Milne believes that if we do nothing in 10-20 years well have a disjointed society made up of polar opposites; ...some in utopia, and many in dystopia: a society that doesn't always move towards new things based on inherent value but perceived value.

There is a lack of frankness in the space according to Milne - a driving force in her writing the book - There's still joy and excitement in realism - in fact, in my opinion, far more than in idealistic futurism. A sentiment more people should agree with after reading Smoke + Mirrors. I wanted to empower more people to feel able to critically think around complex topics and engage in the crucial debates happening in science and tech.

Milne is aScience & Technology Journalist (Forbes), a Keynote Speaker and the Co-Founder of Science: Disrupt. You can order SMOKE & MIRRORS on Amazon and follow her on Twitter@gemmamilne.

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Why The Way You Talk About Artificial Intelligence Needs To Change - Forbes

More than 3 billion peoplearound 50 percent of the worlds populationengage with and post content online. Some of that content is misleading and potentially harmful, whether by design or as a side effect of its spread and manipulation. With the billions of daily active users on social media platforms, even if a mere 0.1 percent of total content contains mis or disinformation, there is a vast volume of content to review.

In response to this challenge, automated content review technologies have emerged as an enticing and scalable solution to help triage mis/disinformation online. Yet, while many technology companies and social media platforms have promoted artificial intelligence (AI) as an omnipotent tactic to address mis/disinformation, AI is not a panacea for information challenges.

In 2015, Microsoft co-founder Bill Gates gave a TED Talk that stressed our lack of preparation for the next pandemic. Fast forward to 2020, and conspiracy theorists have wrongfully used Gates TED talk as evidence to suggest that the novel coronavirus was his doing. The Gates conspiracy fueled misleading posts promoting alarming behavior, such as the creation of a dangerous and ineffective homemade, bleach-based Miracle Mineral Solution for preventing COVID-19. In this case, the repurposing of Gates words and actions made it harder for people to find and discern reliable guidance when they needed it.

The Gates conspiracy exemplifies what the World Health Organization describes as the online infodemic drawing an almost eerie linkage between the public health effects of both the viral dynamics of biology and those of online content. Ironically, these false narratives spread on the very devices and platforms that Gates and his technology sector colleagues created. These technology platformsmany of which are now powered by AI enable the viral spread of a range of mis and disinformation.

Despite what some AI evangelists may claim, AI is not equipped to independently interpret content and judge mis/disinformation. Identifying the many flavors of mis/disinformation often requires nuanced, human assessment, especially when compared to other forms of problematic content.

Content judgments are inherently complex, and they are particularly difficult during COVID-19. As the health and scientific communitys understanding of the virus continues to evolve minute by minute, the boundaries of accurate and misleading information dynamically shift. What was misleading last week (wear a mask for protection) is now the most up to date, accurate information. Further complicating an AI response, much mis/disinformation often maintains a bit of truth and authenticity. A satirical video created with no intention to cause harm may convey a point through sarcasm, or it may fool viewers who do not understand the satire. When a conspiracy theorist tweets a link to the Gates TED talk with their own misleading caption, the meaning of the original, authentic TED talk video changes with that added context.

Even if our AI systems, or AI systems in combination with human review, could perfectly and accurately adjudicate content, at the end of the day, it is human beings that must make sense of that content online. While labeling content has been touted as an effective mechanism for providing adequate disclosure to audiences making sense of material on platforms, thereby mitigating the impact of mis/disinformation, very little is known about the consequences of these labels and how audiences respond to them. Even if platforms label videos or images as manipulated, users may not understand the video to be misleading. Or worse, users may judge video or images from credible sources which do not have labeling as inherently false.

Any efforts considering broad deployment of AI systems for detecting mis/disinformation must seek to understand the human factors that are deeply integral to the success of this work. During periods of global pandemics such as COVID-19, it becomes even more vital to involve diverse, global voices of real people in both the micro content review processes and macro policy and technology decisions around mis/disinformation. Mis/disinformation challenges must be addressed in collaboration with those in civil society, media entities, and alongside social and behavioral science researchers who are already considering the human dynamics affecting public discourse. Coordinating across these disparate skillsets is difficult, but it is vital to capturing the humanistic complexity of mis/disinformation challenges that often fall on technology companies.

COVID-19 obeys the laws of science and biology, but it also affects the human body and psyche in equally impactful doses. And while mis/disinformation has certain dynamics that can be embedded into artificially intelligent systems, it also emerges in formats and contexts that are only interpretable by human judgment. Technology companies must not think about combatting mis/disinformation solely as an engineering challenge. It is a challenge that involves complex human dynamics and requires the involvement of sectors and organizations well beyond Seattle and Silicon Valley.

Claire Leibowicz is program lead on Partnership on AI.

Excerpt from:
Artificial Intelligence is not the cure for the COVID-19 infodemic | TheHill - The Hill

Artificial intelligence (AI) refers to the ability of machines to interpret data and act intelligently, meaning they can make decisions and carry out tasks based on the data at hand rather like a human does.

5 Reasons Why Artificial Intelligence Really Is Going To Change Our World

Chances are youve read a lot about AI in recent years particularly how its going to save the world and/or end civilization as we know it. Its certainly true that AI attracts a lot of hype and, shall we say, colorful predictions. But, unlike some technology trends, much of the hype surrounding AI is entirely justified. It truly is a transformative technology one that will dramatically alter our lives in very real ways.

Not quite convinced? Here are five reasons why I believe AI is going to change the world in 2020 and beyond.

1. AI is everywhere

Ever asked Alexa for the morning weather report, or passed through a public space that uses facial recognition technology, or paid for something using your credit card, or bought a product recommended to you by Amazon, or browsed potential love matches on a dating app? Of course, you have. Most of us have done one or all of these things, probably in the last week. Probably in the last 24 hours.

And, you guessed it, all of these everyday processes are underpinned by AI and data. AI allows your credit card company to determine in the blink of an eye that your latest transaction fits your spending pattern and isnt fraudulent. Mastercard, for example, uses AI algorithms to assess the 75 billion transactions a year processed on its network. So, to put it bluntly, AI is already deeply embedded in your everyday life, and its not going anywhere.

2. AI isnt just infiltrating everyday life, its going to transform entire industries

The impact of AI is already being felt in a wide range of industries, from banking and retail to farming and manufacturing. In healthcare, AI is being used to identify (and, in some cases, even predict) disease, helping healthcare providers and their patients make better treatment and lifestyle decisions.

AI systems can even outperform human experts when it comes to identifying disease; in January 2020, clinical trials of AI software developed by Google Health confirmed that the software was better at spotting signs of breast cancer in mammograms than radiologists. The system also flagged fewer false positive results than the experts.

3. AI will make us more human, not less

As machines become more intelligent, they can carry out more and more tasks leading to rising automation across most industries. With this rise in automation comes valid concerns about the impact on human jobs. But, while theres no doubt that automation will lead to the displacement of many jobs, I believe it will also create new jobs jobs that value our uniquely human capabilities like creativity and empathy.

AI will also make our working lives better. Journalism is one industry thats undergoing an AI revolution, and there are many AI tools that help journalists identify and write stories. At Forbes, for example, an AI-driven content management system called Bertie is used to identify real-time trending topics, suggest improvements to headlines, and identify relevant images. This reduces some of the behind-the-scenes legwork for human journalists, leaving them to focus on telling the story.

4. AI is becoming more affordable for the masses

It used to be that to work with AI youd need expensive technology and a huge team of in-house data scientists. Thats no longer the case. Like many technology solutions, AI is now readily available on an as-a-service basis with a rapidly growing range of off-the-peg service solutions aimed at businesses of all sizes.

As an example, in 2019, Amazon launched Personalize, an AI-based service that helps businesses provide tailored customer recommendations and search results. Incredibly, Amazon says no AI experience is needed to train and deploy the technology.

5. AI fuels other technology trends

Finally, as if we needed any more evidence that AI really is going to change the world, lets end with this simple fact: AI is the foundation on which many other technology trends are built.

Essentially, this means that, without AI, we wouldnt have achieved the amazing recent advances seen in areas like virtual reality, chatbots, facial recognition, autonomous vehicles, and robotics (and thats just to name a few). Think of almost any recent transformative technology or scientific breakthrough, and, somewhere along the way, AI has played a role. For example, thanks to AI, researchers can now read and sequence genes quickly, and this knowledge can be used to determine which drug therapies will be more effective for individual patients.

AI is just one of 25 technology trends that I believe will transform our society. Read more about these key trends in my new book, Tech Trends in Practice: The 25 Technologies That Are Driving The 4th Industrial Revolution.

Read more from the original source:
5 Reasons Why Artificial Intelligence Really Is Going To Change Our World - Forbes

Artificial intelligenceis believed to have the ability to function and perform like that of human intelligence and possess the capability of reasoning, arguing, perceiving and acting rationally. We can say to an extent it can imitate all human functioning but this belief in itself is paradoxical.

As we talk about it on the global platform itself, the views are bifurcated some believe it as next disruptive technology which would lead to development and growth whereas some hold the view that it may lead to job losses and increase unemployment.

Researches have been made on AI towards developing such machines which can imitate human cognitive and logical skills.

Many countries have already adopted AI in judicial litigations,according to CEPEJ and the court administration of Latvia held a conference on Artificial Intelligence at the service of the judiciary, on 27th Sep 2018, it formed a platform which collaborated representatives of the academic world, professional justices, from different European countries to discuss the relevance of AI in the judicial arena, to ensure delivery of improved quality of justices, while maintaining the key fundamental principles and further highlighted the directives on which application of AI will be based upon in judicial system.

Is AI more effective? can it replace lawyers and judges?

AI is something not new as such it has already being used by the judicial system all around the world like in U.S.A to lessen the burden from the judicial system, predictive algorithms are currently being applied. various technologies such as historical crime statistics and facial recognition are also used. It is expected that this kind of automation will bring greater effectiveness in their judicial structure.

In China city of Beijing is claiming to have introduced AI-powered judge which is proclaimed to be the first of a kind and is expected to bring forth a new evolution in the era of the judiciary, it has introduced an internet-based litigation services centre which features AI judges.

Can we trust the decisions made by the AI?

The answer is still contradictory, though AI can make a different kind of legal decisions, this need not mean to be completely authentic.

The AI which is based upon using predictive algorithms by making the use of machine language, tend to rely upon the already existing uploaded data or earlier historical information. Its accuracy depends upon the quality and type of data fed in it.

As it was deduced from the research of 2008 that AI has smarter and greater potential to work efficiently being much faster and better at identifying legal issues than human intelligence could ever do. It enables the mechanization of decision making without human interferences, this goes in favour of the argument that AI can be used in the legal sector or at least it can help in giving a conclusion in legal precedences.

Can AI take the place of judges?

As it is a fact that AI has been used all over the world legislative system. They are capable of fulfilling partial work orsharing of burdens of judges and lawyersbut we cant ultimately depend on them.

Though some argue that AI can be seen as anunbiased mechanismin solving some legal cases where judgments will solely depend upon the facts, existing legal precedents which is earlier expected from human judges but as it is believed that humans judgement power could be affected by prejudice, unconsciousness and biased despite the best of their efforts put in.

Though certain things remain in the principality of philosophical debate which is at par from the knowledge of computers.

While on the other hand, some affirm that there are certain prominent roles of the judges in deciding on convicts sentence/ punishment. This could range from minor to major decisions like that of imposing long term imprisonment or the death penalty.

Those decisions embark upon certain guidelines of conviction which take into consideration the propensity of crime, its influence upon the victims, history of conviction, in this AI has already rendered its diligent role in catering fair judgements.

Judges have the power to ignore the suggestions made by AI, but that will become insignificant if human judges are completely wiped off from the legal system.

Unbiased essence of AI judges: is it feasible?

The artificial intelligence of decisions making which works on algorithms and codes are expected to be unbiased, for this aspect it sets AI as the perfect example for displaying effective legal decisions, but that sounds quite awkward because nothing can be so perfect in its true sense. So that goes with AI as well, which has to be coded by humans only, thus human intelligence is needed to manifest the super artificial intelligence into the legal framework. This can, in turn, create a condition of overlaps or unintended bias.

AI may inherent the human trait of biases of making an error then how these issues will ever solve?

The only way is not to be over-dependent on AI, they should not be treated as a final result in itself.

Can human judge supersede the decision made by AI?

So, the final answer to such questions remains uncertain as to how the government or judiciary will adopt which of these technologies.

Some benefits attached with that are trials may get faster and better with an improvised mechanism of data analysis, the overall performance and effectiveness of the legal system will improve, like that in advisory roles or gathering pieces of evidence, quite prompt decisions can be expected.Here the ethics accompanied with fairness, accountability is vital aspects of the wide acceptance of AI, which could be enabled by setting up of centre for studies on technological sustainability(CSTS) and decision-making capabilities.

But the legal fraternity cant completely rely upon AI for the final judgement and sentencing decisions. Finally, the law and the legal system needs the guidance of human intelligence. As with every passing time and advancement in society, the legal system has improved and upgraded to meet the latest need of human civilization.

Link:
Is AI a More Sustainable Option Than Human Intelligence In Delivering Faster And Effective Justice In India? - India Legal

The markets are historically volatile during thecoronaviruspandemic, throwing conventional risk management strategiesout the window.

Demanddissipated as investors and traders reduced risk into the drop.

The Financial Learning Information Platform, or iFlip, a leading algorithmic intelligence wealth manager, said its trading strategies saved investors 28% in losses in the crash.

What Is iFip?

iFlip is a wealth management app that leverages mathematics to hedge investors out of the market when the risk of a trade is not worth the reward.

The firms investing tools and proprietary trading methods were pioneered by leading Wall Street Trader Kelly Korshak, iFlips co-founder, who worked at the CME Group Inc (NASDAQ: CME), CBOT, Tudor Group, Brevan Howard, and Deutsche Bank AG (NYSE: DB).

Through the use of AI, we are now providing access to long-term investment vehicles that were not available in the past, Korshak said in a statement to Benzinga in January.

The platform reduces uncertainty through statistical modeling and AI, helping iFlips S&P 500 investors generate returns in excess of 400%, over a 15-year period.*

AI Pulls Back Before The Crash

In November, iFlips algorithmic intelligence removed investors from the market due to the risk of near-term reversion.

We got out originally in November of 2010, said Korshak. The algorithmic intelligence felt that the market was overbought.

Back then, the coronavirus was not a concern. Instead, regardless of fundamentals, downside risks exceeded the upside reward on a go-forward basis.

We believe price tells all, said the iFlip co-founder. Fundamental information is interpreted instantly by the markets electronically. We know that information is coming out in real-time and were able to respond to it in real-time.

The market had already made a substantial move from its prior lows, and the probability of a reversion to the mean was too great, he said.

After a small sell-off, the AI re-entered the market in December and defined risk via a dynamic stop.

Navigating The Crash

The ultimate bottom was 32.3%, said Korshak. Literally inside of weeks, ... we lost more S&P 500 points than in the 2008 financial crisis.

On a non-algorithmic basis, active managers suffered tremendously in the crash, further weighed down by fees and mandates, according to iFlip.

The reason mutual funds dont do well beyond their high fee structures is that they are also subject to mandate, said Korshak.

A mutual fund may have a mandate that forces the manager to have 90% of monies invested at any given time to satisfy redemption requests. In passive ETF investments its all-in-all-the-time."

If the market is dropping and a trader takes 90% of the losses, it doesn't do them any favors, he said.

The iFlip AI did suffer some damage in the crash, giving up close to 5%.

Korshak said he made a note about cross-asset volatility and the old mantra that diversification wins out in a fight against losses.

Weve learned this over many occasions over the last 30 years geographical diversification is meaningless over time as we become a global society, he said.

Growing, learning algorithms will always be superior to an actively managed account, the co-founder said. "Even though the correlation of bond and equity prices is still negative, the diversifying element in term of price offsets will eventually decouple in a sustained low-interest-rate environment."

And Korshak is right: when the U.S. market began its sell-off in February, global assets became increasingly correlated, with emerging markets and commodities taking thedive together.

iFlip's Future

iFlip is capitalizing on the recent market crash to introduce a recovery portfolio, an average dollar-weighted custom ETF.

The product is composed of stocks such as Exxon Mobil Corporation (NYSE: XOM), AT&T Inc (NYSE: T), Carnival Corp (NYSE: CCL), Bank of America Corp (NYSE: BAC) andChefs Warehouse Inc (NASDAQ: CHEF).

"If theres a recovery, we think that portfolio will double, and if we think mathematically it is more likely to double than to lose half,it is a bet we should look to take," Korshak said.

If the portfolio were to endure unforeseen volatility, then the AI would leverage its proprietary algorithms to manage risk and hedge out investors, he said.

To learn more about iFlips proven trading systems, visit iflipinvest.com.

*Results over 15 years are hypothetical. Past performance is no guarantee of future performance.

Photo courtesy of iFlip.

See more from Benzinga

2020 Benzinga.com. Benzinga does not provide investment advice. All rights reserved.

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How Artificial Intelligence Helped iFlip Save Investor Returns During The 2020 Market Crash - Yahoo Finance

DUBLIN--(BUSINESS WIRE)--The "Artificial Intelligence in Agriculture Market by Technology (Machine Learning, Computer Vision, and Predictive Analytics), Offering (Software, Hardware, AI-as-a-Service, and Services), Application, and Geography - Global Forecast to 2026" report has been added to ResearchAndMarkets.com's offering.

The AI in the agriculture market is projected to grow at a CAGR of 25.5% from 2020 to 2026.

The AI in agriculture market growth is propelled by the increasing implementation of data generation through sensors and aerial images for crops, increasing crop productivity through deep-learning technology, and government support for the adoption of modern agricultural techniques. However, the high cost of gathering precise field data restrains the market growth. Developing countries, such as China, Brazil, and India, are likely to provide an opportunity for the players in the AI in agriculture market due to the increasing use of unmanned aerial vehicles/drones by these countries in their agricultural farms.

By technology, the machine learning segment is estimated to account for the largest share of the AI in the agriculture market during the forecast period.

Machine learning-enabled solutions are being significantly adopted by agricultural organizations and farmers worldwide to enhance farm productivity and to gain a competitive edge in business operations. In the coming years, the application of machine learning in various agricultural practices is expected to rise exponentially.

By offering, the AI-as-a-Service segment is projected to register the highest CAGR from 2020 to 2026.

Increasing demand for machine learning tool kits and applications that are available in AI-based services, along with benefits, such as advanced infrastructure at minimal cost, transparency in business operations, and better scalability, is leading to the growth of the AI-as-a-Service segment.

By application, the precision farming segment held the largest market size in 2019.

Precision farming involves the usage of innovative artificial intelligence (AI) technologies, such as machine learning, computer vision, and predictive analytics tools, for increasing agriculture productivity. It comprises a technology-driven analysis of data acquired from the fields for increasing crop productivity. Precision farming helps in managing variations in the field accurately, thus enabling the growth of more crops using fewer resources and at reduced production costs. Precision devices integrated with AI technologies help in collecting farm-related data, thereby helping the farmers make better decisions and increase the productivity of their lands.

Market Dynamics

Drivers

Restraints

Opportunities

Challenges

Companies Profiled

For more information about this report visit https://www.researchandmarkets.com/r/r54x8l

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Artificial Intelligence in Agriculture Market by Technology, Offering, Application, and Geography - Global Forecast to 2026 - ResearchAndMarkets.com -...

The report on the Artificial Intelligence Systems Spending market provides a birds eye view of the current proceeding within the Artificial Intelligence Systems Spending market. Further, the report also takes into account the impact of the novel COVID-19 pandemic on the Artificial Intelligence Systems Spending market and offers a clear assessment of the projected market fluctuations during the forecast period. The different factors that are likely to impact the overall dynamics of the Artificial Intelligence Systems Spending market over the forecast period (2019-2029) including the current trends, growth opportunities, restraining factors, and more are discussed in detail in the market study.

As per the presented market report, the global Artificial Intelligence Systems Spending market is projected to attain a CAGR growth of ~XX% during the assessment period and surpass a value of ~US$XX by the end of 20XX. Further, the report suggests that the growth of the Artificial Intelligence Systems Spending market hinges its hope on a range of factors including, emphasis on innovation by market players, surge in the investments pertaining to R&D activities, and favorable regulatory policies among others.

Get Free Sample PDF (including COVID19 Impact Analysis, full TOC, Tables and Figures) of Market Report @ https://www.researchmoz.com/enquiry.php?type=S&repid=2569485&source=atm

Competition Landscape

The report provides critical insights related to the business operations of prominent companies operating in the Artificial Intelligence Systems Spending market. The revenue generated, market presence of different companies, product range, and the financials of each company is included in the report.

Regional Landscape

The regional landscape section of the report provides resourceful insights related to the scenario of the Artificial Intelligence Systems Spending market in the key regions. Further, the market attractiveness of each region provides players a clear understanding of the overall growth potential of the Artificial Intelligence Systems Spending market in each region.

End-User Analysis

The report provides a detailed analysis of the various end-users of the Artificial Intelligence Systems Spending along with the market share, size, and revenue generated by each end-user.

The following manufacturers are covered:Google Inc.Microsoft CorporationFacebook, Inc.IBM CorporationApple Inc.Amazon.com Inc.Intel CorporationInfosys LimitedWipro LtdSalesforce.com Inc.IPsoft Inc.

Segment by RegionsNorth AmericaEuropeChinaJapanSoutheast AsiaIndia

Segment by TypeDeep LearningMachine LearningNatural Language ProcessingMachine VisionAGIASI

Segment by ApplicationBFSIDiscrete & Process ManufacturingRetail & WholesaleMedia & EntertainmentTelecommunications & UtilitiesOthers

Do You Have Any Query Or Specific Requirement? Ask to Our Industry [emailprotected] https://www.researchmoz.com/enquiry.php?type=E&repid=2569485&source=atm

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End-use Industries of Artificial Intelligence Systems Spending Product Witness Unparalleled Slowdown Induced by Global Outbreak of COVID-29 - Cole of...

As engineers and researchers work on developing and perfecting their machine learning and AI algorithms, the end goal is ultimately to recreate the human brain. The most perfect AI imaginable would be able to process the world around us through typical sensory input but leverage the storage and computing strengths of supercomputers.

With that end goal in mind, it's not hard to understand the ways that AI is evolving as it continues to be developed. Deep learning AI is able to interpret patterns and derive conclusions. In essence, it's learning how to mimic the way that humans process the world around us.

That said, from the onset, AIs generally need typical computer input, like coded data. Developing AIs that can process the world through audio and visual input, sensory input, is a much harder task.

In order to understand artificial intelligence in the context of a perception-based interface, we need to understand what the end goal is. We need to understand how the brain is modeled and works.

Our brains are essentially the world's most powerful supercomputers, except for the fact that they're made out of organic material, rather than silicon and other materials.

Our right brain is largely perception-based, it's focused on the interpretation of environmental inputs like taste, feel, sound, sight, etc. Our left brain, on the other hand, is focused on rational thought. Our senses provide patterns to our right brain, and to our left brain, those senses provide the rationale for decision making. In a sense, we have two AIs in our head that work together to create a logical, yet also emotionally swayed machine.

RELATED: ELON MUSK SHARES HIS VIEWS ON AI, NEURALINK, AUTOPILOT, AND THE BLUE DOT IN A PODCAST

Human intelligence and our definition of what an intelligent thing is all drawback to how we ourselves process the world. In order for artificial intelligence to truly succeed, that is to be the best version of itself that it can be, then it needs to be intelligent from a human perspective.

All this draws back to modern AI in a simple way, AI is programmed in how to make a decision. Machine learning algorithms allow code to be pseudo-organically generated so that algorithms can "learn" in a sense. All of this programming is based on reasoning, on "if, then, do this."

Arguably, our brain's decision-making process is just as much based on emotions and feeling as it is reason. Emotional intelligence is a significant portion of what makes intelligence. It's the ability to read a situation, to understand other human's emotions and reactions. In order for AIs to evolve and be the best possible algorithm, they need to be able to process sensory input and emotion.

Most artificial intelligence systems are primarily created on the foundation of deep learning algorithms. This is the means of exposing a computer program to thousands of examples and AI learning how to solve problems through this process. Deep learning can be boiled down to teaching a computer how to be smart.

After any given deep learning phase for an AI, the system can perceive the inputs that it was trained on and make decisions therein. The decision-making tree that the AI forms from traditional deep learning mimics the way the right side of our brain works. It is based on the perception of inputs, of pseudo-senses.

RELATED: ARTIFICIAL INTELLIGENCE PROVES TO BE 10 PERCENT FASTER AND MORE EFFICIENT THAN HUMAN LAWYERS

Deep learning is a way of getting computers to reason, not just with if-then statements, but through the understanding of the situation. That said, the current situations AI are being trained on aren't as complex as interpreting a conversation with Becky to see if she's into you. Rather it's more along the lines of is this a dark cat, a black bag, or the night sky. Primitive, but still sensory perception...

While deep learning is currently heavily focused on one pathway, meaning AIs are developing specialties, eventually it won't be too far fetched to start training AIs on multiple things at once. Just like a toddler might learn colors and numbers at the same time. Expanding this out, as computer processing power grows, perhaps accelerated by practical quantum computing, there's no question that AIs will evolve to become more human.

Advanced AI will continue to deal with understanding and processing patterns from the world around us. Through this, it will develop more complex models on how to process that information. In a sense, AIs are like toddlers, but soon they're going to be teenagers, and eventually, they may graduate with a doctorate. All figuratively of course... though, an age where an AI graduates a university probably isn't that far off.

RELATED: ELON MUSK AND OPEN AI WANT TO CREATE AN ARTIFICIAL INTELLIGENCE THAT WON'T SPELL DOOM FOR HUMANITY

When we think about intelligent humans, we usually think of the most rationally minded people. Yet, we miss out on what is so unique about human intelligence creativity. In a sense, we take for granted our creativity, yet it is the thing that makes us the most intelligent of living beings. Our ability to process situations, not just understand what the sum of two numbers is, is what makes us uniquely intelligent. So uniquely intelligent that we can design and create artificially intelligent beings that will soon be able to match our human intelligence.

While modern AIs are primarily focused on singular strands of intelligence, whether that be finding which picture contains a bicycle or which email is spam, we're already training AIs to be all-around smart, humanly smart.

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Artificial Intelligence is Evolving to Process the World Like Humans - Interesting Engineering

By Ms Samara Kitchener

Airborne search and rescue is an expensive and demanding task, but what if there was a better way?

AI-Search, Defences Artificial Intelligence (AI) prototype to transform airborne search and rescue, is now in its second phase of development.

The prototype is a collaboration between the Royal Australian Air ForcesPlan Jericho, Warfare Innovation Navy Branch andAir Mobility Groups 35 Squadron. The system, which combines a sensor and processor, is highly portable and has the potential to enable any aircraft, includingUnmanned Aerial Systems (UAS), vehicles or vessels to become an improvised search and rescue platform.

A recentC-27J Spartansortie fromRAAF Base Amberleyoff the coast of Stradbroke Island, with the assistance of the Australian Volunteer Coast Guard, helped evaluate the AI-Search algorithm to recognise a life raft and other waterborne vessels. This sortie was the second of several phases to develop and evaluate this proof of concept.

The AI-Search algorithms are being developed by budding machine learning expert, Lieutenant Harry Hubbert from the Warfare Innovation Navy Branch.

During the sortie, we had a few GoPro sensors rigged up to detect a life raft and two algorithmic approaches working together to increase accuracy and the likelihood of a detection, Lieutenant Hubbert said.

This sortie was pretty challenging as the life raft was upside down, making it harder to see for both the human eye and the AI-Search sensors.

The sensors are trained to detect an orange top, rather than a black top, but the AI-Search still had a 70 per cent detection rate, compared to the human detection rate of around 50 per cent.

The 30 per cent AI-Search non-detections happened when there was low contrast between dark water and the black underside of the life raft, and the good news is that we had no false positives.

Flying Officer Katherine Mitchell, piloting the aircraft as part of a search and rescue training exercise said that it was hard to see the upside-down life raft.

We barely saw it 50 per cent of the time, Flying Officer Mitchell said.

AI-Search is already picking up more than what we are seeing, its incredible and it doesnt get fatigued.

Wing Commander Michael Gan, Plan Jerichos AI lead said that they are now taking their learnings, finding the strengths and weaknesses, and iterating the next version.

The next phase will involve testing different sensor and processor combinations in a range of environmental conditions, with the potential of testing on a range of aircraft, including UAS, Wing Commander Gan said.

This article was published by the Royal Australian Navy on April 21, 2020.

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Artificial Intelligence Enabled Airborne Search and Rescue - Second Line of Defense

Artificial Intelligence has already become an integral part of many tasks in the workforce. Until now, it has been limited to functions like data analysis and other tasks that require purely logical thinking, but with the combination of high-level graphic design, Artificial Intelligence is now making its way to the more human side of the workforce in the form of digital humans, or avatars.

AI and Cognitive Technology company IPSoft has already implemented their version, named Amelia, in a number of industries. A global telecom provider hired Amelia as an IT Operations Expert, handling 82 percent of the companys service desk requests and has had more than 80,000 conversations with employees. Roughly 70 percent of queries have been handled by Amelia on her own, which means that human employees are able to focus their attention on other high-level tasks. Amelia has been implemented in other forms as a customer service agent and human resources operative.

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Avatars are not only limited to handling internal operations. Early-stage tech company Agora Brands Group aims to broaden the engagement and functionality of interactive avatars. Targeting industries such as self-service kiosks, deathcare, and celebrity recreation, Agora Brands Group plans to make their AI interactive avatars accessible to a wide range of consumers. Using Natural Language Processing (NLP), text-to-speech technology, and smart analytics, avatars can increase businesses top- and bottom-lines as well as create a positive personalized and more engaging customer experience.

Beyond convenience and customer service, interactive avatars also serve a public health function. A 2018 study by Metro found that all eight touchscreen kiosks at McDonalds restaurants that were tested found harmful bacteria on the screens. Thousands of customers touch these screens every day, greatly increasing the potential for diseases to spread. Replacing touchscreen kiosks with interactive avatar kiosks all but eliminates this certain health risk.

Read more:Why COVID-19 Is Putting Pressure on E-Commerce Teams to Perform

Digital human company Uneeq trained a version of Sophie to help users with questions regarding the COVID-19 crisis. This platform makes it easy for users to get the information they need in a way that emulates natural conversation with a Healthcare professional. Applications like this also open the door to other similar uses of avatars as engaging digital assistants with medical and mental health institutions.

It may be a few years before interactive avatars are widely implemented, but the potential for their usefulness is only limited by the motivation of businesses to use them. Whether in the workforce or in personal uses, employees and consumers can expect to see more and more avatars engaging with their everyday activities very soon.

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Interactive Avatars are the New Era of Artificial Intelligence - AiThority