Artificial intelligence robots are slowly replacing blue collar and white collarworkers.

Go to the trading floors to find out that there are no human brokers. Algorithmic trading software makes money for most investment funds.

It takes 0.2 seconds for a price quote to come from the exchange to your software vendors data center (DC), 0.3 seconds from the data center to reach your trading screen, 0.1 seconds for your trading software to process this received quote, 0.3 seconds for it to analyze and place a trade, 0.2 seconds for your trade order to reach your broker, 0.3 seconds for your broker to route your order to the exchange.

Total time elapsed = 0.2 + 0.3 + 0.1 + 0.3 + 0.2 + 0.3 = 1.4 seconds. In todays dynamic trading world, the original price quote would have changed multiple times within this 1.4 second period. The algorithmic trading program is to identify profitable opportunities and place the trades to generate profits at a speed and frequency that is impossible to match by a human trader.

No chance for humans to compete with machines in computing speed and efficiency, instant analysis and decision-making.

The human-AI replacement process is following the causal link of smart work automation:

Office workers -> Distant workers -> RPA bots -> Digital workers -> AI workers

"A digital worker is software designed to model and emulate human job roles by performing end-to-end job activities using automation and AI-based skills. In contrast, an RPA bot is software that mimics human actions by performing specific tasks for which its programmed.

Digital workers can understand, decide and act to automate job roles as opposed to simply acting to automate individual tasks. In doing so, they extend RPA capabilities and can be applied to more use cases". IBM DW

The human-robot replacement has been accelerated by COVID-19. Google as Facebook let their employees continue to work from home through July 2021 due to the ongoing COVID-19 pandemic. Most of them will hardly return back.

As McKinsey cautiously projected:

"The activities most susceptible to automation are physical ones in highly structured and predictable environments, as well as data collection and processing.

In the United States, these activities make up 51 percent of activities in the economy, accounting for almost $2.7 trillion in wages.

They are most prevalent in manufacturing, accommodation and food service, and retail trade. And its not just low-skill, low-wage work that could be automated; middle-skill and high-paying, high skill occupations, too, have a degree of automation potential".

View post:
Will Artificial Intelligence Robots Do the Majority of Our Work in the Coming Decade? - BBN Times

A team of San Diego scientists is harnessing artificial intelligence to understand why COVID-19 symptoms can vary dramatically from one person to the next information that could prove useful in the continued fight against the coronavirus and future pandemics.

Researchers pored through publicly available data to see how other viruses alter which genes our cells turn on or off. Using that information, they found a set of genes activated across a wide range of infections, including the novel coronavirus. Those genes predicted whether someone would have a mild or a severe case of COVID-19, and whether they were likely to have a lengthy hospital stay.

A UC San Diego-led team joined by researchers at Scripps Research and the La Jolla Institute for Immunology published the findings June 11. The studys authors say their approach could help determine whether new treatments and vaccines are working.

When the whole world faced this pandemic, it took several months for people to scramble to understand the new virus, said Dr. Pradipta Ghosh, a UCSD cell biologist and one of the studys authors. I think we need more of this computational framework to guide us in panic states like this.

The project began in March 2020, when Ghosh teamed up with UCSD computer scientist Debashis Sahoo to better understand why the novel coronavirus was causing little to no symptoms in some people while wreaking havoc on others.

There was just one problem: The novel coronavirus was, well, novel, meaning there wasnt much data to learn from.

So Sahoo and Ghosh took a different tack. They went to public databases and downloaded 45,000 samples from a wide array of viral infections, including Ebola, Zika, influenza, HIV, and hepatitis C virus, among others.

Their hope was to find a shared response pattern to these viruses, and thats exactly what they saw: 166 genes that were consistently cranked up during infection. Among that list, 20 genes generally separated patients with mild symptoms from those who became severely ill.

The coronavirus was no exception. Sahoo and Ghosh say they identified this common viral response pattern well before testing it in samples from COVID-19 patients and infected cells, yet the results held up surprisingly well.

It seemed to work in every data set we used, Sahoo said. It was hard to believe.

They say their findings show that respiratory failure in COVID-19 patients is the result of overwhelming inflammation that damages the airways and, over time, makes immune cells less effective.

Stanfords Purvesh Khatri isnt surprised. His lab routinely uses computer algorithms and statistics to find patterns in large sets of immune response data. In 2015, Khatris group found that respiratory viruses trigger a common response. And in April, they reported that this shared response applied to a range of other viruses, too, including the novel coronavirus.

That makes sense, Khatri says, because researchers have long known there are certain genes the immune system turns on in response to virtually any viral infection.

Overall, the idea is pretty solid, said Khatri of the recent UCSD-led study. The genes are all (the) usual suspects.

Sahoo and Ghosh continue to test their findings in new coronavirus data as it becomes available. Theyre particularly interested in COVID-19 long-haulers. Ghosh says theyre already seeing that people with prolonged coronavirus symptoms have distinct gene activation patterns compared to those whove fully recovered. Think of it like a smoldering fire that wont die out.

The researchers ultimate hope isnt just to predict and understand severe disease, but to stop it. For example, they say, a doctor could give a patient a different therapy if a blood sample suggests theyre likely to get sicker with their current treatment. Ghosh adds that the gene pattern theyre seeing could help identify promising new treatments and vaccines against future pandemics based on which therapies prevent responses linked to severe disease.

In unknown, uncharted territory, this provides guard rails for us to start looking around, understand (the virus), find solutions, build better models and, finally, find therapeutics.

More:
Can artificial intelligence predict how sick you'll get from COVID-19? UC San Diego scientists think so - The San Diego Union-Tribune

AI is a term that is used frequently in the horticultural industry. Because of this, one sometimes gets the impression that AI is going to solve all future problems in the industry. Think, for example, of the shortage of employees and specifically trained growers. Will AI then ensure that all work can be taken over by robots in the future? A dream for some, but also a horror scenario for many. Is AI going to replace people? "The answer to that, in our opinion, is no," says Ton van Dijk with LetsGrow.com. In this article, he explains how AI can contribute to horticulture.

LetsGrow.com believes that AI helps to make better decisions, but it is certainly not going to replace a grower or crop advisor. AI does bring the grower and crop advisor possibilities to control larger areas from a distance. Expert Knowledge combined with Artificial Intelligence is the golden combination. But more about that later.

So how exactly does AI work?It is often thought that as much data as possible from different growers together is the recipe for developing an algorithm, an algorithm that might make automatic cultivation possible. However, this is not the right way. Firstly, because data of growers always remains the property of the grower and cannot be used in this way. Secondly, because using data from the past to develop algorithms and predictions for the future, does not ensure optimization. In that situation, one takes any mistakes from the past into development. AI makes it possible to perform tasks faster and sometimes better than people, but only if the algorithm is built properly.

So what about horticulture?Over the past 50 years, much scientific research has been done on plant physiology and physics in and around a greenhouse. As a result of this research, LetsGrow.com knows how a plant grows and how to make the plant as comfortable as possible. Optimum conditions ultimately ensure a high-quality crop and a higher yield. Growers and cultivation advisors use their own experiences and calculations for this, which is also called Expert Intelligence (EI). Systems can already be set up to issue alerts to the grower when needed. This already ensures that growers can focus on more strategic matters. LetsGrow.com contributes to this by visualizing and analyzing the growers data. These results make it possible to cultivate in a data-driven way. But it can be even more extensive.

Combining outcomes from EI with other external data, such as weather forecasts, for example, it is possible to use those data to create an optimal situation for the plant. For example, it is possible to use an optimally constructed Machine Learning model to predict when plant stress may occur in the plant. A grower can therefore use this information to adjust the cultivation strategy to prevent this plant stress from actually occurring. In this way, the combination of data, EI, and AI helps to provide predictive insight to the grower. This gives the grower the possibility to create an even more stable and optimal crop.

There are so many more possibilities with AI. Think of automatic image recognition by placing cameras in the greenhouse. Together with partner Gearbox, LetsGrow.com is already making this possible. In addition, LetsGrow.com also works together with HortiKey and their Plantalyzer. This is a robot that rides along the pipe-rail and takes pictures of the crop. The advanced AI in this robot is able to recognize the number of fruits or flowers in the path or to analyze growth. This recognition makes it possible for LetsGrow.com to make more accurate yield predictions and visualize them in the dashboard MyLetsGrow. A grower can use this data to work in a more targeted way by, for example, using the sales of his product correctly and selling at higher margins.

So is AI now taking over from humans?No. At all times it is important that the combination between computer and human remains. A combination of Expert Intelligence and Artificial Intelligence. Humans determine the strategy at the start of the process based on the commercial requirements for that year. In addition, humans must always be able to intervene in case of calamities. AI therefore certainly does not make people superfluous, but it does enable people to manage and optimize larger areas without being too preoccupied with peripheral issues.

For more information:LetsGrow.cominfo@letsgrow.com http://www.letsgrow.com

Originally posted here:
The sense and nonsense of Artificial Intelligence in the greenhouse - hortidaily.com

Our last AI post on this blog, the New (if Decidedly Not 'Final') Frontier ofArtificial Intelligence Regulation, touched on both the FederalTrade Commission's (FTC) April 19, 2021, AI guidance and the EuropeanCommission's proposed AI Regulation. The FTC's 2021guidance referenced, in large part, the FTC's April 2020 post"UsingArtificial Intelligence and Algorithms." The recent FTCguidance also relied on older FTC work on AI, including a January2016 report, "Big Data: A Tool for Inclusion orExclusion?," which in turn followed a September 15, 2014,workshop on the same topic. The Big Data workshop addressed datamodeling, data mining and analytics, and gave us a prospective lookat what would become an FTC strategy on AI.

The FTC's guidance begins with the data, and the 2016guidance on big data and subsequent AI development addresses thismost directly. The 2020 guidance then highlights importantprinciples such as transparency, explain-ability, fairness,accuracy and accountability for organizations to consider. And the2021 guidance elaborates on how consent, or opt-in, mechanisms workwhen an organization is gathering the data used for modeldevelopment.

Taken together, the three sets of FTC guidance - the 2021, 2020,and 2016 guidance ? provide insight into the FTC's approach toorganizational use of AI, which spans a vast portion of the datalife cycle, including the creation, refinement, use and back-endauditing of AI. As a whole, the various pieces of FTC guidance alsoprovide a multistep process for what the FTC appears to view asresponsible AI use. In this post, we summarize our takeaways fromthe FTC's AI guidance across the data life cycle to provide apractical approach to responsible AI deployment.

Evaluation of a data set should assess the quality ofthe data (including accuracy, completeness and representativeness)? and if the data set is missing certain population data, theorganization must take appropriate steps to address and remedy thatissue (2016).

An organization must honor promises made to consumersand provide consumers with substantive information about theorganization's data practices when gathering information for AIpurposes (2016). Any related opt-in mechanisms for such datagathering must operate as disclosed to consumers (2021).

An organization should recognize the data compilationstep as a "descriptive activity," which the FTC definesas a process aimed at uncovering and summarizing "patterns orfeatures that exist in data sets" - a reference to data mining scholarship (2016) (note that theFTC's referenced materials originally at mmds.org are nowredirected).

Compilation efforts should be organized around a lifecycle model that provides for compilation and consolidation beforemoving on to data mining, analytics and use (2016).

An organization must recognize that there may beuncorrected biases in underlying consumer data that will surface ina compilation; therefore, an organization should review data setsto ensure hidden biases are not creating unintended discriminatoryimpacts (2016).

An organization should maintain reasonable security overconsumer data (2016).

If data are collected from individuals in a deceitful orotherwise inappropriate manner, the organization may need to deletethe data (2021).

An organization should recognize the model and AIapplication selection step as a predictive activity, where anorganization is using "statistical models to generate newdata" - a reference to predictive analytics scholarship (2016).

An organization must determine if a proposed data modelor application properly accounts for biases (2016). Where there areshortcomings in the data model, the model's use must beaccordingly limited (2021).

Organizations that build AI models may "not selltheir big data analytics products to customers if they know or havereason to know that those customers will use the products forfraudulent or discriminatory purposes." An organization must,therefore, evaluate potential limitations on the provision or useof AI applications to ensure there is a "permissiblepurpose" for the use of the application (2016).

Finally, as a general rule, the FTC asserts that underthe FTC Act, a practice is patently unfair if it causes more harmthan good (2021).

Organizations must design models to account for datagaps (2021).

Organizations must consider whether their reliance onparticular AI models raises ethical or fairness concerns(2016).

Organizations must consider the end uses of the modelsand cannot create, market or sell "insights" used forfraudulent or discriminatory purposes (2016).

Organizations must test the algorithm before use (2021).This testing should include an evaluation of AI outcomes(2020).

Organizations must consider prediction accuracy whenusing "big data" (2016).

Model evaluation must focus on both inputsand AI models may not discriminate against aprotected class (2020).

Input evaluation shouldinclude considerations of ethnically based factors or proxies forsuch factors.

Outcome evaluation iscritical for all models, including facially neutral models.

Model evaluation should consider alternative models, asthe FTC can challenge models if a less discriminatory alternativewould achieve the same results (2020).

If data are collected from individuals in a deceptive,unfair, or illegal manner, deletion of any AI models or algorithmsdeveloped from the data may also be required (2021).

Organizations must be transparent and not misleadconsumers "about the nature of the interaction" ? and notutilize fake "engager profiles" as part of their AIservices (2020).

Organizations cannot exaggerate an AI model'sefficacy or misinform consumers about whether AI results are fairor unbiased. According to the FTC, deceptive AI statements areactionable (2021).

If algorithms are used to assign scores to consumers, anorganization must disclose key factors that affect the score,rank-ordered according to importance (2020).

Organizations providing certain types of reports throughAI services must also provide notices to the users of such reports(2016).

Organizations building AI models based on consumer datamust, at least in some circumstances, allow consumers access to theinformation supporting the AI models (2016).

Automated decisions based on third-party data mayrequire the organization using the third-party data to provide theconsumer with an "adverse action" notice (for example, ifunder the Fair Credit Reporting Act 15 U.S.C. 1681(Rev. Sept. 2018), such decisions deny an applicant anapartment or charge them a higher rent) (2020).

General "you don'tmeet our criteria" disclosures are not sufficient. The FTCexpects end users to know what specific data areused in the AI model and how the data are used bythe AI model to make a decision (2020).

Organizations that change specific terms of deals basedon automated systems must disclose the changes and reasoning toconsumers (2020).

Organizations should provide consumers with anopportunity to amend or supplement information used to makedecisions about them (2020) and allow consumers to correct errorsor inaccuracies in their personal information (2016).

When deploying models, organizations must confirm thatthe AI models have been validated to ensure they work as intendedand do not illegally discriminate (2020).

Organizations must carefully evaluate and select anappropriate AI accountability mechanism, transparency frameworkand/or independent standard, and implement as applicable(2020).

An organization should determine the fairness of an AImodel by examining whether the particular model causes, or islikely to cause, substantial harm to consumers that is notreasonably avoidable and not outweighed by countervailing benefits(2021).

Organizations must test AI models periodically torevalidate that they function as intended (2020) and to ensure alack of discriminatory effects (2021).

Organizations must account for compliance, ethics,fairness and equality when using AI models, taking into accountfour key questions (2016; 2020):

How representative is thedata set? Does the AI model account for biases? How accurate are the AI predictions? Does the reliance on the data set raise ethical or fairnessconcerns?

Organizations must embrace transparency andindependence, which can be achieved in part through the following(2021):

Using independent,third-party audit processes and auditors, which are immune to theintent of the AI model. Ensuring data sets and AI source code are open to externalinspection. Applying appropriate recognized AI transparency frameworks,accountability mechanisms and independent standards. Publishing the results of third-party AI audits.

Organizations remain accountable throughout the AI datalife cycle under the FTC's recommendations for AI transparencyand independence (2021).

The content of this article is intended to provide a generalguide to the subject matter. Specialist advice should be soughtabout your specific circumstances.

Read this article:
The Not-So-Hidden FTC Guidance On Organizational Use Of Artificial Intelligence (AI), From Data Gathering Through Model Audits - Technology - United...

But theres another crucial observation here. Intelligence was never an endpoint for evolution, something to aim for. Instead, it emerged in many different forms from countless tiny solutions to challenges that allowed living things to survive and take on future challenges. Intelligence is the current high point in an ongoing and open-ended process. In this sense, evolution is quite different from algorithms the way people typically think of themas means to an end.

Its this open-endedness, glimpsed in the apparently aimless sequence of challenges generated by POET, that Clune and others believe could lead to new kinds of AI. For decades AI researchers have tried to build algorithms to mimic human intelligence, but the real breakthrough may come from building algorithms that try to mimic the open-ended problem-solving of evolutionand sitting back to watch what emerges.

Researchers are already using machine learning on itself, training it to find solutions to some of the fields hardest problems, such as how to make machines that can learn more than one task at a time or cope with situations they have not encountered before. Some now think that taking this approach and running with it might be the best path to artificial general intelligence.We could start an algorithm that initially does not have much intelligence inside it, and watch it bootstrap itself all the way up potentially to AGI, Clune says.

The truth is that for now, AGI remains a fantasy. But thats largely because nobody knows how to makeit.Advances in AI are piecemeal and carried out by humans, with progress typically involving tweaks to existing techniques or algorithms, yielding incremental leaps in performance or accuracy. Clune characterizes these efforts as attempts to discover the building blocks for artificial intelligence without knowing what youre looking for or how many blocks youll need. And thats just the start. At some point, we have to take on the Herculean task of putting them all together, he says.

Asking AI to find andassemble those building blocks for usis a paradigm shift. Its saying we want to create an intelligent machine, but we dont care what it might look likejust give us whatever works.

Even if AGI is never achieved, the self-teaching approach may still change what sorts of AI are created. The world needsmore than a very good Go player, says Clune. For him, creating a supersmart machine means building a system that invents its own challenges, solves them, and then invents new ones. POET is a tiny glimpse of this in action. Clune imagines a machine that teaches a bot to walk, then to play hopscotch, then maybe to play Go. Then maybe it learns math puzzles and starts inventing its own challenges, he says. The system continuously innovates, and the skys the limit in terms of where it might go.

Link:
AI is learning how to create itself - MIT Technology Review