AI News, Artificial Intelligence Enables a Data Revolution artificial intelligence
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The advent of deep learning, the power and possibilities of big data, and the plummeting cost of computing power and storage, have made machine-learning technologies as ubiquitous as they are transformative.
We’ll explore the social, economic and security implications of these types of emerging technologies, and discuss policy prescriptions needed to balance rewards against disruption and ensure the democratization of gains.
Artificial Intelligence Needs To Reset
I had the chance to interview my colleague at ArCompany, Karen Bennet, a seasoned engineering executive in platform technology, open and closed source systems and artificial intelligence technology.
A former engineeringlead from Yahoo!, and part of the original team who brought Red Hat to success, Karen evolved with the technological revolution, utilizing AI in expert systems in her early IBM days, and is currently laying witness to the rapid experimentation in machine learning and deep learning.
Earlier this spring the death of a pedestrian to a self-driving vehicle raised alarms that went beyond the technology and called to question the ethics or lack thereof behind the decisions of an automated system.
The trolley problem is not a simple binary choice between the life of one person to save 5 people but rather evolves into a debate of conscience, emotion and perception thatnow complicates the path to which a reasonable decision can be made by a machine.
To use history as a predictor, both cloud and the dot net industries took about 5 years before they started impacting people in a significant way, and almost 10 years before these industries influenced major shifts in the market.
This recent article substantiates the widespread AIpilots that are more common today: Vendors of AI technology are often incentivized to make their technology sound more capable than it is – but hint at more real-world traction than they actually have… Most AI applications in the enterprise are little more than ‘pilots.’ Primarily, vendor companies that sell marketing solutions, healthcare solutions and finance solutions in artificial intelligence are simply test-driving the technology.
However, as part of this process, if some the data are erroneously labelled, or if there is not enough data representation, or if there are problematic data signifying bias, bad decision-making results are likely to occur.She also attests current processes continue to be refined: Currently, AI is all about decision support, to provide insights into a form for which business can draw conclusions.
In the next phase of AI, which automates actionsfrom the data, there are additional issues that need to be addressed like bias, explainability, privacy, diversity, ethics, and continuous model learning.
Joy Buolamwini, and MIT graduate and Founder of Algorithmic Justice League in this latest interview called for a moratorium on this technology stating it was ineffective and needed more oversight, and has appealedfor more government standards into these types of systems before they are publicly released.
Mindset and culture are elements of these legacy systems that provide a systemic view into the established process, values, and business rules that have dictated, not only how organizations operate, but also why these ingrained elements will create significant hurdles for business, especially when things are currently humming nicely.
Companies are learning that focus should be on: 1) diversity in the data, which includes proper representation across populations 2) ensuring diverse experiences, perspectives and thinking into the creation algorithms 3) prioritizing quality of the data over than quantity These are important especially as bias is introduced and trust and confidence in data degrade.
The data may be continuously changing and AI models require filters to prevent incorrect labeling such as an image of a black man being labeled as a gorilla or a panda becoming labelled as a gibbon.
However, for other organizations, the masses of data can represent a risk because of the divergent sources and formats that make it more difficult to transform the information: emails, system logs, web pages, customer transcripts, documents, slides, informal chats, social networks and exploding rich media like images and video.Data transformation continues to be a stumbling block towards developing clean data sets, hence effective models.
Bias exists in many business models to minimize risk assessments, and optimize targeting opportunities and while they may produce profitable business results, they have been known to result in unintended consequences that cause individual harm and deepen economic disparities.
There is a heightened caution surrounding bias because the introduction of AI will not only perpetuate existing biases, the result from these learning models may generalize to the point it will deepen the economic and societal divide.
The types of questions used in the initial COMPAS research revealed enough human bias that the system perpetuated recommendations that unintentionally treated blacks, who would never go on to re-offend, more harshly by law than white defendants, who would go on to re-offend and were treated more leniently at the time of sentencing.
This article confirmed, 'A Popular Algorithm Is No Better at Predicting Crimes Than Random People'… If this software is only as accurate as untrained people responding to an online survey, I think the courts should consider that when trying to decide how much weight to put on them in making decisions Karen stipulated, While we try to fix existing systems to minimize this bias, it is critical that models train on diverse sets of data to prevent future harms.
Given the potential risks to faulty models pervading business and society, businesses do not have governance mechanisms to police for unfair or immoral decisions that will inadvertently impact the end consumer.
The prevailing factor of AI ensures societal benefits, including streamlining processes, increasing convenience, improving products and services, and detecting potential harms through automation.
As discussions and news about AI persist, this term, “AI” coupled with “ethics” reveals increasingly grave concerns where AI technology can inflict societal damage that will test human conscience and values.
In a world of increasing automation and deliberate progress towards increasing cognitive computing capabilities, the impending AI winter has afforded business the necessary time to determine how AI fits into their organization and the problems it wants to solve.
This culmination of business readiness, regulation, education, and research are necessary to bring both business and consumers up to speed and to ensure a regulatory system is in place to properly constrain technology and one that leaves humanity at the helm a little while longer.
The Promise of Artificial Intelligence-Enabled IoT
Advances in artificial intelligence (AI) and machine learning coupled with more robust and competent devices is driving transformation across industries and workstreams, from small farms in India to huge corporations in the United States.
But as Steve summarizes in his interview, there have been three crucial breakthroughs that have pivotally propelled AI and the ability for computers to interact with their environments in a more human-like way.
In his interview, Steve calls out a great example of how edge devices are transforming even industries not traditionally considered to be technologically advanced, such as farming.
They need data on soil temperature, density, and moisture—all vital information for making decisions around when to plant or rotate crops.
Transforming job opportunities Of course, while IoT is enabling business across every industry in countless unique scenarios, like every technology innovation it is also generating shifts in the job market.
By working together to plan for changes in the job market, employees can train to acquire the skills they need to transition into new roles, while employers get qualified employees who can drive the business forward.
Attend IoT in Action – February 13 in San Francisco As IoT continues to advance, we can expect to see increasingly affordable and accessible AI-enabled solutions across all industries and in every size of a business in countries all around the world.
Why Artificial Intelligence will enable new scientific discoveries
In materials science, as in other branches of experimental science such as drug discovery, we are able to obtain ever larger amounts of data from ever more sophisticated experiments and modelling.
Our current approaches to data in science fall well short of the advanced machine learning techniques social media platforms use to recognize the friend in a photo we’ve uploaded or what film we might like to watch next.
A discovery worthy of a Nobel Prize The fourth goal has already been articulated as a Grand Challenge for biomedical science, by Hiroaki Kitano, Director of Sony Science Laboratories, “to develop an AI system that can make major scientific discoveries in biomedical sciences and that is worthy of a Nobel Prize and far beyond.” (see: https://www.aaai.org/ojs/index.php/aimagazine/article/view/2642 ).
By analysing the results of new experiments and new material discoveries we can further tune our approaches, saving time, money and improving outcomes.
This could make a significant difference in the cost of production for advanced materials, allowing them to move from being used only in expensive one-off pieces of equipment into high volume production for new consumer devices.
Artificial Intelligence techniques in science will be applied to quantum technologies, batteries, solid state lighting, nanoelectronics and nanomechanics, for high throughput screening of new materials in both simulation and in experiments, for computer vision in microscopy, radiography and tomography and for optimisation of data-rich manufacturing operations.
However, the types of machine learning techniques that will work best in aiding scientific discovery are complex and will require computing hardware that is much more efficient and flexible than exists today.
Today the internet is driven by advertising revenue and so it is also natural to see a lot of AI research focused on improving basic commercial imperatives such as understanding social media feeds or improving internet search.
Silicon based computers may only have another 10-20 years of advances ahead and so we need to accelerate work on new materials and on the next breakthroughs that will come from quantum computing or eventually from molecular computing.
Drugs that are able to directly reach the diseased tissues and avoid wider dispersion in the body will result in people suffering far fewer side effects.
More importantly, new machine learning techniques applied to scientific discovery will quickly enable complex research challenges to be addressed that could never be solved by humans alone working within feasible time limits and resources.
- On 14. april 2021
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