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Last year, an AI named Benjamin wrote a weird and entertaining science fiction short film called Sunspring. Now, Benjamin’s back in a new film titled It’s No Game. Like its predecessor, the short is a surprisingly effective blend of human and machine talent—plus a healthy dose of the surreal.
Watch the film below to see David Hasselhoff, compelled by nanobots, reel off algorithmically mashed up lines from Knight Rider and Baywatch scripts.

Artificial intelligence is perhaps a bit overhyped currently. Rapid progress in a variety of difficult AI problems has us, at times, confusing future possibilities with present capability. The AI timeline tends to go missing, and human obsolescence in the face of superintelligence appears imminent.
Because AI competes with our proudest assets, such as intelligence and creativity, the response is fearful. What exactly is our value in a world where we’re outperformed by algorithms at basically everything? It’s a fascinating and important question. There is no answer. But we have time to figure it out.
AI is still the narrow type. Most algorithms are excellent, even superhuman, at the task for which they're designed, but ill-suited for anything else. And there are yet some tasks just beyond AI's reach. Writing is one of them. It’s No Game is self-aware enough to call out the worry—writers replaced by robots—right next to the still glitchy (but awesome) output of an artificial neural network.
Benjamin’s writing relies on whatever content is fed into it. In this case, instead of X Files scripts (as in Sunspring), we’re treated to multiple segments inspired by Shakespeare, Golden Age Hollywood, Aaron Sorkin’s fast-paced politics, and of course, Baywatch and Knight Rider.
The output tends toward the nonsensical, but mostly, that's okay. Quick, dense lines from Aaron Sorkin’s work, for example, can be as much about the emotional sense communicated by the actors as they are about content.
"People will watch a Sorkin movie and not take in what’s being said, [but] understand the thrust of the scene and know what’s going on," says the Walking Dead's Thomas Payne, who plays one of the screenwriters in the film.
Which is basically why Benjamin’s stuff works here. It’s up to the cast and crew’s sense of timing and delivery to make the lines meaningful. David Hasselhoff time travels to make those disembodied 80s snippets into an unmistakable resurrection of “the Hoff” himself. And the freaked out, confused scene at the end ironically echoes our larger existential worries.
We may be headed into a world of artificial general intelligence, and that world may arrive faster than conservative guesses suggest. But make no mistake, even narrow AI is very powerful. And artists, entrepreneurs, and researchers will no doubt continue to work with such algorithms to make surprising new creations, from the purely useful to the bizarre and fascinating.
(Check out Annalee Newitz's article in Ars Technica for an excellent and comprehensive behind-the-scenes look at the making of It's No Game.)
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Smithers Rapra, one of the world’s leading sources of information on polymers, plastics, rubber and adhesives, recently published a report stating that the global tire manufacturing industry is set to grow almost at a pace of four percent per year from now through to 2022. There are many key factors and industry trends that are driving this growth, but a large percent of that growth can be contributed to the innovations in tire technology which are then furthering advancements in automation within tire manufacturing and distribution facilities.
These advancements are not only benefiting new facilities (Greenfields), but also existing facilities (Brownfields) that were developed with only manual operations in mind. Large tire retailers are beginning to realize what automating their entire facilities or even a few parts of their handling process can do to their bottom lines. Over the last decade, automation has touched each area in the tire manufacturing/handling process and also allowing distribution center operators to realize what this can do to their bottom lines as well.
Automating Each Step in the Process
Legacy Brownfields were designed with manual processes for the handling of their tires, and as a result have typically been perceived as automation-unfriendly. However, with advancements in automation, modules can be customized to each process and each facility, causing an accelerated adoption rate.

Breaking Down the Process Areas
The first process is the automatic storage and transportation of raw rubber and other basic ingredients. During this process, raw materials are placed into automatic storage and automatic retrieval (AS/RS) systems.
Raw materials are then delivered by automation to various mixing and preparation processes to be made into compounded materials for component preparation.
These materials are then delivered by automation to component preparation machines.
The next step is the green tire building process, which receives components automatically.
From there green tires are automatically unloaded from tire building machines (TBMs) by robots.
The green tires are then automatically moved by green tire storage and retrieval gantries or crane style AS/RS systems.
Automatic guided vehicles (AGV) or an overhead electrified monorail systems (EMS) then deliver green tires to the curing press.
After curing, final quality checks on the finished products are completed within fully automated cells.
The tires are then sorted and placed on pallets using gantry robots.
Order fulfillment and shipping automation is the final step. This is where the customer orders are picked by gantry systems and then sequenced into a trailer for customer delivery.
For facilities that do not have the ability to automate all at once, they can deploy modular automation in sequential steps. Modular automation is able to be built and configured in elemental steps to meet their planning requirements. The systems are modular from raw materials to palletizing. There’s a shorter delivery and installation time due to this standardization concept and can be easily applicable to any factory.
Experiencing the Benefits
Whether automating part or all of the handling process, customers are seeing the necessary return on investments (ROI). They’ve begun to see the following savings and benefits:
Total process flow control and visualization of manufacturing/distribution processes at each stage.
Increase in productivity. For example, customers can manufacture 5 to 10 percent more tires per day by getting the right Work-in-Process (WIP) and/or product in the right place at the right time.
With complete communication between the facility’s WMS, MES and ERP systems as well as interface to PLCs, robot controllers and upper host, facilities have 100 percent tracing of total tire production history. This means they can quickly see the progression of the tires from materials and components to green tires (GT) to finished tires (FT).
Real-time data for inventory management, which enables 100 percent availability of materials, components and tires in each process.
With an optimized layout design, less space is needed. This means customers can minimize the required factory footprint, which in turn minimizes the need for large capital investments.
Just-in-Time (JIT) delivery to different processes areas as well as to the end customer.
Streamlined material flow that allows facilities managers to minimize buffering, have less WIP and maximize efficiency.
Reduction in waste and scrap.
Better ergonomics and allocation of human resources. This results in fewer health and safety labor-related problems.
Flexibility with operations and processes. Customers have ability to make automatic changes based on needs and demands.

Implementing for the Future
With rising customer demands and tire sales predicted to spike, tire manufactures are seeking ways to maintain their share of the market by implementing the latest automated systems or upgrading individual processes, which in turn gives them more of a chance to maintain/gain market share.
Because they’re tailored for each manufacturing and distribution process and can be installed in new and existing plants (as either a one-time deployment or independently through scalable, modular automation implementations), the time is now to automate. It ensures your bottom line keeps stacking up.

Author Bio:
Don Heelis is a mechanical engineer and senior systems sales manager for Cimcorp, a manufacturer and integrator of turnkey robotic gantry-based order fulfillment and tire handling solutions. With more than 25 years of experience, Heelis helps customers develop fully automated systems that increase efficiency, accuracy and throughput for manufacturing, warehouses and distribution centers around the world.
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The duty of man who investigates the writings of scientists, if learning the truth is his goal, is to make himself an enemy of all that he reads and … attack it from every side. He should also suspect himself as he performs his critical examination of it, so that he may avoid falling into either prejudice or leniency.
– Ibn al-Haytham (965-1040 CE)
Science is in the midst of a data crisis. Last year, there were more than 1.2 million new papers published in the biomedical sciences alone, bringing the total number of peer-reviewed biomedical papers to over 26 million. However, the average scientist reads only about 250 papers a year. Meanwhile, the quality of the scientific literature has been in decline. Some recent studies found that the majority of biomedical papers were irreproducible.
The twin challenges of too much quantity and too little quality are rooted in the finite neurological capacity of the human mind. Scientists are deriving hypotheses from a smaller and smaller fraction of our collective knowledge and consequently, more and more, asking the wrong questions, or asking ones that have already been answered. Also, human creativity seems to depend increasingly on the stochasticity of previous experiences – particular life events that allow a researcher to notice something others do not. Although chance has always been a factor in scientific discovery, it is currently playing a much larger role than it should.
One promising strategy to overcome the current crisis is to integrate machines and artificial intelligence in the scientific process. Machines have greater memory and higher computational capacity than the human brain. Automation of the scientific process could greatly increase the rate of discovery. It could even begin another scientific revolution. That huge possibility hinges on an equally huge question: can scientific discovery really be automated?
I believe it can, using an approach that we have known about for centuries. The answer to this question can be found in the work of Sir Francis Bacon, the 17th-century English philosopher and a key progenitor of modern science.
The first reiterations of the scientific method can be traced back many centuries earlier to Muslim thinkers such as Ibn al-Haytham, who emphasised both empiricism and experimentation. However, it was Bacon who first formalised the scientific method and made it a subject of study. In his book Novum Organum (1620), he proposed a model for discovery that is still known as the Baconian method. He argued against syllogistic logic for scientific synthesis, which he considered to be unreliable. Instead, he proposed an approach in which relevant observations about a specific phenomenon are systematically collected, tabulated and objectively analysed using inductive logic to generate generalisable ideas. In his view, truth could be uncovered only when the mind is free from incomplete (and hence false) axioms.
The Baconian method attempted to remove logical bias from the process of observation and conceptualisation, by delineating the steps of scientific synthesis and optimising each one separately. Bacon’s vision was to leverage a community of observers to collect vast amounts of information about nature and tabulate it into a central record accessible to inductive analysis. In Novum Organum, he wrote: ‘Empiricists are like ants; they accumulate and use. Rationalists spin webs like spiders. The best method is that of the bee; it is somewhere in between, taking existing material and using it.’
The Baconian method is rarely used today. It proved too laborious and extravagantly expensive; its technological applications were unclear. However, at the time the formalisation of a scientific method marked a revolutionary advance. Before it, science was metaphysical, accessible only to a few learned men, mostly of noble birth. By rejecting the authority of the ancient Greeks and delineating the steps of discovery, Bacon created a blueprint that would allow anyone, regardless of background, to become a scientist.
Bacon’s insights also revealed an important hidden truth: the discovery process is inherently algorithmic. It is the outcome of a finite number of steps that are repeated until a meaningful result is uncovered. Bacon explicitly used the word ‘machine’ in describing his method. His scientific algorithm has three essential components: first, observations have to be collected and integrated into the total corpus of knowledge. Second, the new observations are used to generate new hypotheses. Third, the hypotheses are tested through carefully designed experiments.
If science is algorithmic, then it must have the potential for automation. This futuristic dream has eluded information and computer scientists for decades, in large part because the three main steps of scientific discovery occupy different planes. Observation is sensual; hypothesis-generation is mental; and experimentation is mechanical. Automating the scientific process will require the effective incorporation of machines in each step, and in all three feeding into each other without friction. Nobody has yet figured out how to do that.
Experimentation has seen the most substantial recent progress. For example, the pharmaceutical industry commonly uses automated high-throughput platforms for drug design. Startups such as Transcriptic and Emerald Cloud Lab, both in California, are building systems to automate almost every physical task that biomedical scientists do. Scientists can submit their experiments online, where they are converted to code and fed into robotic platforms that carry out a battery of biological experiments. These solutions are most relevant to disciplines that require intensive experimentation, such as molecular biology and chemical engineering, but analogous methods can be applied in other data-intensive fields, and even extended to theoretical disciplines.
Automated hypothesis-generation is less advanced, but the work of Don Swanson in the 1980s provided an important step forward. He demonstrated the existence of hidden links between unrelated ideas in the scientific literature; using a simple deductive logical framework, he could connect papers from various fields with no citation overlap. In this way, Swanson was able to hypothesize a novel link between dietary fish oil and Reynaud’s Syndrome without conducting any experiments or being an expert in either field. Other, more recent approaches, such as those of Andrey Rzhetsky at the University of Chicago and Albert-László Barabási at Northeastern University, rely on mathematical modeling and graph theory. They incorporate large datasets, in which knowledge is projected as a network, where nodes are concepts and links are relationships between them. Novel hypotheses would show up as undiscovered links between nodes.
The most challenging step in the automation process is how to collect reliable scientific observations on a large scale. There is currently no central data bank that holds humanity’s total scientific knowledge on an observational level. Natural language-processing has advanced to the point at which it can automatically extract not only relationships but also context from scientific papers. However, major scientific publishers have placed severe restrictions on text-mining. More important, the text of papers is biased towards the scientist’s interpretations (or misconceptions), and it contains synthesised complex concepts and methodologies that are difficult to extract and quantify.
Nevertheless, recent advances in computing and networked databases make the Baconian method practical for the first time in history. And even before scientific discovery can be automated, embracing Bacon’s approach could prove valuable at a time when pure reductionism is reaching the edge of its usefulness.
Human minds simply cannot reconstruct highly complex natural phenomena efficiently enough in the age of big data. A modern Baconian method that incorporates reductionist ideas through data-mining, but then analyses this information through inductive computational models, could transform our understanding of the natural world. Such an approach would enable us to generate novel hypotheses that have higher chances of turning out to be true, to test those hypotheses, and to fill gaps in our knowledge. It would also provide a much-needed reminder of what science is supposed to be: truth-seeking, anti-authoritarian, and limitlessly free.

This article was originally published at Aeon and has been republished under Creative Commons.
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Artificial intelligence systems that learn from human language acquire the same gender and racial biases as people, according to a new study. Continue reading →