AI News, Magical thinking about machine learning won’t bring the reality of AI any closer | John Naughton

Magical thinking about machine learning won’t bring the reality of AI any closer | John Naughton

An array of radar and light-emitting lidar sensors allowed onboard algorithms to calculate that, given their host vehicle’s steady speed of 43mph, the object was six seconds away – assuming it remained stationary.

In her 2016 book Weapons of Math Destruction, Cathy O’Neil, a former math prodigy who left Wall Street to teach and write and run the excellent mathbabe blog, demonstrated beyond question that, far from eradicating human biases, algorithms could magnify and entrench them.

So much attention has been focused on the distant promises and threats of artificial intelligence, AI, that almost no one has noticed us moving into a new phase of the algorithmic revolution that could be just as fraught and disorienting – with barely a question asked.

Between the “dumb” fixed algorithms and true AI lies the problematic halfway house we’ve already entered with scarcely a thought and almost no debate, much less agreement as to aims, ethics, safety, best practice.

What I found was extraordinary: a human-made digital ecosystem, distributed among racks of black boxes crouched like ninjas in billion-dollar data farms – which is what stock markets had become.

Where once there had been a physical trading floor, all action had devolved to a central server, in which nimble, predatory algorithms fed off lumbering institutional ones, tempting them to sell lower and buy higher by fooling them as to the state of the market.

they were doing invisible battle at the speed of light, placing and cancelling the same order 10,000 times per second or slamming so many into the system that the whole market shook – all beyond the oversight or control of humans.

I travelled to Chicago to see a man named Eric Hunsader, whose prodigious programming skills allowed him to see market data in far more detail than regulators, and he showed me that by 2014, “mini flash crashes” were happening every week.

Significantly, Johnson’s paper on the subject was published in the journal Nature and described the stock market in terms of “an abrupt system-wide transition from a mixed human-machine phase to a new all-machine phase characterized by frequent black swan [ie highly unusual] events with ultrafast durations”.

The scenario was complicated, according to the science historian George Dyson, by the fact that some HFT firms were allowing the algos to learn – “just letting the black box try different things, with small amounts of money, and if it works, reinforce those rules.

Then you actually have rules where nobody knows what the rules are: the algorithms create their own rules – you let them evolve the same way nature evolves organisms.” Non-finance industry observers began to postulate a catastrophic global “splash crash”, while the fastest-growing area of the market became (and remains) instruments that profit from volatility.

“You’re right on point,” he told me: a new form of algorithm is moving into the world, which has “the capability to rewrite bits of its own code”, at which point it becomes like “a genetic algorithm”.

That’s the issue.” To underscore this point, Johnson and a team of colleagues from the University of Miami and Notre Dame produced a paper, Emergence of Extreme Subpopulations from Common Information and Likely Enhancement from Future Bonding Algorithms, purporting to mathematically prove that attempts to connect people on social media inevitably polarize society as a whole.

“I’ve been looking out for these algorithms, too,” she says, “and I’d been thinking: ‘Oh, big data hasn’t gotten there yet.’ But more recently a friend who’s a bookseller on Amazon has been telling me how crazy the pricing situation there has become for people like him.

That must be the equivalent of a flash crash!’” Anecdotal evidence of anomalous events on Amazon is plentiful, in the form of threads from bemused sellers, and at least one academic paper from 2016, which claims: “Examples have emerged of cases where competing pieces of algorithmic pricing software interacted in unexpected ways and produced unpredictable prices, as well as cases where algorithms were intentionally designed to implement price fixing.” The problem, again, is how to apportion responsibility in a chaotic algorithmic environment where simple cause and effect either doesn’t apply or is nearly impossible to trace.

When a driver ran off the road and was killed in a Toyota Camry after appearing to accelerate wildly for no obvious reason, Nasa experts spent six months examining the millions of lines of code in its operating system, without finding evidence for what the driver’s family believed had occurred, but the manufacturer steadfastly denied – that the car had accelerated of its own accord.

Only when a pair of embedded software experts spent 20 months digging into the code were they able to prove the family’s case, revealing a twisted mass of what programmers call “spaghetti code”, full of algorithms that jostled and fought, generating anomalous, unpredictable output.

“What I was obsessed with 20 years ago that has completely taken over the world today are multicellular, metazoan digital organisms, the same way we see in biology, where you have all these pieces of code running on people’s iPhones, and collectively it acts like one multicellular organism.

“There’s this old law called Ashby’s law that says a control system has to be as complex as the system it’s controlling, and we’re running into that at full speed now, with this huge push to build self-driving cars where the software has to have a complete model of everything, and almost by definition we’re not going to understand it.

Because any model that we understand is gonna do the thing like run into a fire truck ’cause we forgot to put in the fire truck.” Unlike our old electro-mechanical systems, these new algorithms are also impossible to test exhaustively.

Dyson questions whether we will ever have self-driving cars roaming freely through city streets, while Toby Walsh, a professor of artificial intelligence at the University of New South Wales who wrote his first program at age 13 and ran a tyro computing business by his late teens, explains from a technical perspective why this is.

It’s going to be taken over by machines that will be far better at doing it than we are.” Walsh believes this makes it more, not less, important that the public learn about programming, because the more alienated we become from it, the more it seems like magic beyond our ability to affect.

When shown the definition of “algorithm” given earlier in this piece, he found it incomplete, commenting: “I would suggest the problem is that algorithm now means any large, complex decision making software system and the larger environment in which it is embedded, which makes them even more unpredictable.” A chilling thought indeed.

This is one of the criticisms of these systems so far, in that it’s not possible to go back and analyze exactly why some decisions are made, because the internal number of choices is so large that how we got to that point may not be something we can ever recreateto prove culpability beyond doubt.” The counter-argument is that, once a program has slipped up, the entire population of programs can be rewritten or updated so it doesn’t happen again – unlike humans, whose propensity to repeat mistakes will doubtless fascinate intelligent machines of the future.

A group of Google employees resigned over and thousands more questioned the tech monolith’s provision of machine learning software to the Pentagon’s Project Maven “algorithmic warfare” program – concerns to which management eventually responded, agreeing not to renew the Maven contract and to publish a code of ethics for the use of its algorithms.

The question is how tech managers can presume to know what their algorithms will do or be directed to do in situ – especially given the certainty that all sides will develop adaptive algorithmic counter-systems designed to confuse enemy weapons.

Tech companies say they’re only improving accuracy with Maven – ie the right people will be killed rather than the wrong ones – and in saying that, the political assumption that those people on the other side of the world are more killable, and that the US military gets to define what suspicion looks like, go unchallenged.

One solution, employed by the Federal Aviation Authority in relation to commercial aviation, is to log and assess the content of all programs and subsequent updates to such a level of detail that algorithmic interactions are well understood in advance – but this is impractical on a large scale.

Not only does it push humans yet further from the process, but Johnson, the physicist, conducted a study for the Department of Defense that found “extreme behaviors that couldn’t be deduced from the code itself” even in large, complex systems built using this technique.

More practically, Spafford, the software security expert, advises making tech companies responsible for the actions of their products, whether specific lines of rogue code – or proof of negligence in relation to them – can be identified or not.

I think the deep scientific thing is that software engineers are trained to write programs to do things that optimize – and with good reason, because you’re often optimizing in relation to things like the weight distribution in a plane, or a most fuel-efficient speed: in the usual, anticipated circumstances optimizing makes sense.

But in unusual circumstances it doesn’t, and we need to ask: ‘What’s the worst thing that could happen in this algorithm once it starts interacting with others?’ The problem is we don’t even have a word for this concept, much less a science to study it.” He pauses for moment, trying to wrap his brain around the problem.

Code-Dependent: Pros and Cons of the Algorithm Age

While many of the 2016 U.S. presidential election post-mortems noted the revolutionary impact of web-based tools in influencing its outcome, XPrize Foundation CEO Peter Diamandis predicted that “five big tech trends will make this election look tame.” He said advances in quantum computing and the rapid evolution of AI and AI agents embedded in systems and devices in the Internet of Things will lead to hyper-stalking, influencing and shaping of voters, and hyper-personalized ads, and will create new ways to misrepresent reality and perpetuate falsehoods.

Analysts like Aneesh Aneesh of Stanford University foresee algorithms taking over public and private activities in a new era of “algocratic governance” that supplants “bureaucratic hierarchies.” Others, like Harvard’s Shoshana Zuboff, describe the emergence of “surveillance capitalism” that organizes economic behavior in an “information civilization.” To illuminate current attitudes about the potential impacts of algorithms in the next decade, Pew Research Center and Elon University’s Imagining the Internet Center conducted a large-scale canvassing of technology experts, scholars, corporate practitioners and government leaders.

As Brian Christian and Tom Griffiths write in Algorithms to Live By, algorithms provide ‘a better standard against which to compare human cognition itself.’ They are also a goad to consider that same cognition: How are we thinking and what does it mean to think through algorithms to mediate our world?

After all, algorithms are generated by trial and error, by testing, by observing, and coming to certain mathematical formulae regarding choices that have been made again and again – and this can be used for difficult choices and problems, especially when intuitively we cannot readily see an answer or a way to resolve the problem.

Our systems do not have, and we need to build in, what David Gelernter called ‘topsight,’ the ability to not only create technological solutions but also see and explore their consequences before we build business models, companies and markets on their strengths, and especially on their limitations.” Chudakov added that this is especially necessary because in the next decade and beyond, “By expanding collection and analysis of data and the resulting application of this information, a layer of intelligence or thinking manipulation is added to processes and objects that previously did not have that layer.

The result: As information tools and predictive dynamics are more widely adopted, our lives will be increasingly affected by their inherent conclusions and the narratives they spawn.” “The overall impact of ubiquitous algorithms is presently incalculable because the presence of algorithms in everyday processes and transactions is now so great, and is mostly hidden from public view.

The expanding collection and analysis of data and the resulting application of this information can cure diseases, decrease poverty, bring timely solutions to people and places where need is greatest, and dispel millennia of prejudice, ill-founded conclusions, inhumane practice and ignorance of all kinds.

In order to make algorithms more transparent, products and product information circulars might include an outline of algorithmic assumptions, akin to the nutritional sidebar now found on many packaged food products, that would inform users of how algorithms drive intelligence in a given product and a reasonable outline of the implications inherent in those assumptions.” A

number of respondents noted the many ways in which algorithms will help make sense of massive amounts of data, noting that this will spark breakthroughs in science, new conveniences and human capacities in everyday life, and an ever-better capacity to link people to the information that will help them.

However, many people – and arguably many more people – will be able to obtain loans in the future, as banks turn away from using such factors as race, socio-economic background, postal code and the like to assess fit.

Moreover, with more data (and with a more interactive relationship between bank and client) banks can reduce their risk, thus providing more loans, while at the same time providing a range of services individually directed to actually help a person’s financial state.

Health care is a significant and growing expense not because people are becoming less healthy (in fact, society-wide, the opposite is true) but because of the significant overhead required to support increasingly complex systems, including prescriptions, insurance, facilities and more.

New technologies will enable health providers to shift a significant percentage of that load to the individual, who will (with the aid of personal support systems) manage their health better, coordinate and manage their own care, and create less of a burden on the system.

They say this is creating a flawed, logic-driven society and that as the process evolves – that is, as algorithms begin to write the algorithms – humans may get left out of the loop, letting “the robots decide.” Representative of this view: Bart Knijnenburg, assistant professor in human-centered computing at Clemson University, replied, “Algorithms will capitalize on convenience and profit, thereby discriminating [against] certain populations, but also eroding the experience of everyone else.

My biggest fear is that, unless we tune our algorithms for self-actualization, it will be simply too convenient for people to follow the advice of an algorithm (or, too difficult to go beyond such advice), turning these algorithms into self-fulfilling prophecies, and users into zombies who exclusively consume easy-to-consume items.” An anonymous futurist said, “This has been going on since the beginning of the industrial revolution.

When you remove the humanity from a system where people are included, they become victims.” Another anonymous respondent wrote, “We simply can’t capture every data element that represents the vastness of a person and that person’s needs, wants, hopes, desires.

sampling of excerpts tied to this theme from other respondents (for details, read the fuller versions in the full report): Algorithms have the capability to shape individuals’ decisions without them even knowing it, giving those who have control of the algorithms an unfair position of power.

The harms of new technology will be most experienced by those already disadvantaged in society, where advertising algorithms offer bail bondsman ads that assume readers are criminals, loan applications that penalize people for proxies so correlated with race that they effectively penalize people based on race, and similar issues.” Dudley Irish, a software engineer, observed, “All, let me repeat that, all of the training data contains biases.

sampling of quote excerpts tied to this theme from other respondents (for details, read the fuller versions in the full report): One of the greatest challenges of the next era will be balancing protection of intellectual property in algorithms with protecting the subjects of those algorithms from unfair discrimination and social engineering.

Ten years from now, though, the life of someone whose capabilities and perception of the world is augmented by sensors and processed with powerful AI and connected to vast amounts of data is going to be vastly different from that of those who don’t have access to those tools or knowledge of how to utilize them.

number of participants in this canvassing expressed concerns over the change in the public’s information diets, the “atomization of media,” an over-emphasis of the extreme, ugly, weird news, and the favoring of “truthiness” over more-factual material that may be vital to understanding how to be a responsible citizen of the world.

Easier said than done, but if there were ever a time to bring the smartest minds in industry together with the smartest minds in academia to solve this problem, this is the time.” Chris Kutarna, author of Age of Discovery and fellow at the Oxford Martin School, wrote, “Algorithms are an explicit form of heuristic, a way of routinizing certain choices and decisions so that we are not constantly drinking from a fire hydrant of sensory inputs.

sampling of quote excerpts tied to this theme from other respondents (for details, read the fuller versions in the full report): We need some kind of rainbow coalition to come up with rules to avoid allowing inbuilt bias and groupthink to effect the outcomes.

I suspect utopia given that we have survived at least one existential crisis (nuclear) in the past and that our track record toward peace, although slow, is solid.” Following is a brief collection of comments by several of the many top analysts who participated in this canvassing: Vinton Cerf, Internet Hall of Fame member and vice president and chief internet evangelist at Google: “Algorithms are mostly intended to steer people to useful information and I see this as a net positive.” Cory Doctorow, writer, computer science activist-in-residence at MIT Media Lab and co-owner of Boing Boing, responded, “The choices in this question are too limited.

If, on the other hand, the practice continues as is, it terminates with a kind of Kafkaesque nightmare where we do things ‘because the computer says so’ and we call them fair ‘because the computer says so.’” Jonathan Grudin, principal researcher at Microsoft, said, “We are finally reaching a state of symbiosis or partnership with technology.

I’m less worried about bad actors prevailing than I am about unintended and unnoticed negative consequences sneaking up on us.” Doc Searls, journalist, speaker and director of Project VRM at Harvard University’s Berkman Center, wrote, “The biggest issue with algorithms today is the black-box nature of some of the largest and most consequential ones.

They will get smaller and more numerous, as more responsibility over individual lives moves away from faceless systems more interested in surveillance and advertising than actual service.” Marc Rotenberg, executive director of the Electronic Privacy Information Center, observed, “The core problem with algorithmic-based decision-making is the lack of accountability.

Compare this with China’s social obedience score for internet users.” David Clark, Internet Hall of Fame member and senior research scientist at MIT, replied, “I see the positive outcomes outweighing the negative, but the issue will be that certain people will suffer negative consequences, perhaps very serious, and society will have to decide how to deal with these outcomes.

Even if they are fearful of the consequences, people will accept that they must live with the outcomes of these algorithms, even though they are fearful of the risks.” Baratunde Thurston, Director’s Fellow at MIT Media Lab, Fast Company columnist, and former digital director of The Onion, wrote: “Main positive changes: 1) The excuse of not knowing things will be reduced greatly as information becomes even more connected and complete.

We’ll need both industry reform within the technology companies creating these systems and far more savvy regulatory regimes to handle the complex challenges that arise.” John Markoff, author of Machines of Loving Grace: The Quest for Common Ground Between Humans and Robots and senior writer at The New York Times, observed, “I am most concerned about the lack of algorithmic transparency.

Because of unhealthy power dynamics in our society, I sadly suspect that the outcomes will be far more problematic – mechanisms to limit people’s opportunities, segment and segregate people into unequal buckets, and leverage surveillance to force people into more oppressive situations.

An honest, verifiable cost-benefit analysis, measuring improved efficiency or better outcomes against the loss of privacy or inadvertent discrimination, would avoid the ‘trust us, it will be wonderful and it’s AI!’ decision-making.” Robert Atkinson, president of the Information Technology and Innovation Foundation, said, “Like virtually all past technologies, algorithms will create value and cut costs, far in excess of any costs.

There are too many examples to cite, but I’ll list a few: would-be borrowers turned away from banks, individuals with black-identifying names seeing themselves in advertisements for criminal background searches, people being denied insurance and health care.

Universities must diversify their faculties, to ensure that students see themselves reflected in their teachers.” Jamais Cascio, distinguished fellow at the Institute for the Future, observed, “The impact of algorithms in the early transition era will be overall negative, as we (humans, human society and economy) attempt to learn how to integrate these technologies.

By the time the transition takes hold – probably a good 20 years, maybe a bit less – many of those problems will be overcome, and the ancillary adaptations (e.g., potential rise of universal basic income) will start to have an overall benefit.

In other words, shorter term (this decade) negative, longer term (next decade) positive.” Mike Liebhold, senior researcher and distinguished fellow at the Institute for the Future, commented, “The future effects of algorithms in our lives will shift over time as we master new competencies.

At an absolute minimum, we need to learn to form effective questions and tasks for machines, how to interpret responses and how to simply detect and repair a machine mistake.” Ben Shneiderman, professor of computer science at the University of Maryland, wrote, “When well-designed, algorithms amplify human abilities, but they must be comprehensible, predictable and controllable.


those formalizations included the Gödel–Herbrand–Kleene recursive functions of 1930, 1934 and 1935, Alonzo Church's lambda calculus of 1936, Emil Post's Formulation 1 of 1936, and Alan Turing's Turing machines of 1936–7 and 1939.

No human being can write fast enough, or long enough, or small enough† ( †'smaller and smaller without limit'd be trying to write on molecules, on atoms, on electrons') to list all members of an enumerably infinite set by writing out their names, one after another, in some notation.

But humans can do something equally useful, in the case of certain enumerably infinite sets: They can give explicit instructions for determining the nth member of the set, for arbitrary finite n.

Thus, Boolos and Jeffrey are saying that an algorithm implies instructions for a process that 'creates' output integers from an arbitrary 'input' integer or integers that, in theory, can be arbitrarily large.

Thus an algorithm can be an algebraic equation such as y = m + n – two arbitrary 'input variables' m and n that produce an output y.

From such uncertainties, that characterize ongoing work, stems the unavailability of a definition of algorithm that suits both concrete (in some sense) and abstract usage of the term.

Many computer programs contain algorithms that detail the specific instructions a computer should perform (in a specific order) to carry out a specified task, such as calculating employees' paychecks or printing students' report cards.

Typically, when an algorithm is associated with processing information, data can be read from an input source, written to an output device and stored for further processing.

Algorithms can be expressed in many kinds of notation, including natural languages, pseudocode, flowcharts, drakon-charts, programming languages or control tables (processed by interpreters).

Pseudocode, flowcharts, drakon-charts and control tables are structured ways to express algorithms that avoid many of the ambiguities common in natural language statements.

There is a wide variety of representations possible and one can express a given Turing machine program as a sequence of machine tables (see more at finite-state machine, state transition table and control table), as flowcharts and drakon-charts (see more at state diagram), or as a form of rudimentary machine code or assembly code called 'sets of quadruples' (see more at Turing machine).

However, algorithms are also implemented by other means, such as in a biological neural network (for example, the human brain implementing arithmetic or an insect looking for food), in an electrical circuit, or in a mechanical device.

In computer systems, an algorithm is basically an instance of logic written in software by software developers to be effective for the intended 'target' computer(s) to produce output from given (perhaps null) input.

An optimal algorithm, even running in old hardware, would produce faster results than a non-optimal (higher time complexity) algorithm for the same purpose, running in more efficient hardware;

Minsky's machine proceeds sequentially through its five (or six, depending on how one counts) instructions, unless either a conditional IF–THEN GOTO or an unconditional GOTO changes program flow out of sequence.

For example, the subprogram in Euclid's algorithm to compute the remainder would execute much faster if the programmer had a 'modulus' instruction available rather than just subtraction (or worse: just Minsky's 'decrement').

Structured programming, canonical structures: Per the Church–Turing thesis, any algorithm can be computed by a model known to be Turing complete, and per Minsky's demonstrations, Turing completeness requires only four instruction types—conditional GOTO, unconditional GOTO, assignment, HALT.

To 'measure' is to place a shorter measuring length s successively (q times) along longer length l until the remaining portion r is less than the shorter length s.[52]

In modern words, remainder r = l − q×s, q being the quotient, or remainder r is the 'modulus', the integer-fractional part left over after the division.[53]

The following algorithm is framed as Knuth's four-step version of Euclid's and Nicomachus', but, rather than using division to find the remainder, it uses successive subtractions of the shorter length s from the remaining length r until r is less than s.

E2: [Is the remainder zero?]: EITHER (i) the last measure was exact, the remainder in R is zero, and the program can halt, OR (ii) the algorithm must continue: the last measure left a remainder in R less than measuring number in S.

This works because, when at last the minuend M is less than or equal to the subtrahend S ( Difference = Minuend − Subtrahend), the minuend can become s (the new measuring length) and the subtrahend can become the new r (the length to be measured);

the domain of the function computed by the algorithm/program, is to include only positive integers including zero, then the failures at zero indicate that the algorithm (and the program that instantiates it) is a partial function rather than a total function.

Proof of program correctness by use of mathematical induction: Knuth demonstrates the application of mathematical induction to an 'extended' version of Euclid's algorithm, and he proposes 'a general method applicable to proving the validity of any algorithm'.[56]

Can the algorithms be improved?: Once the programmer judges a program 'fit' and 'effective'—that is, it computes the function intended by its author—then the question becomes, can it be improved?

For example, a binary search algorithm (with cost O(log n) ) outperforms a sequential search (cost O(n) ) when used for table lookups on sorted lists or arrays.

However, ultimately, most algorithms are usually implemented on particular hardware / software platforms and their algorithmic efficiency is eventually put to the test using real code.

For the solution of a 'one off' problem, the efficiency of a particular algorithm may not have significant consequences (unless n is extremely large) but for algorithms designed for fast interactive, commercial or long life scientific usage it may be critical.

To illustrate the potential improvements possible even in well established algorithms, a recent significant innovation, relating to FFT algorithms (used heavily in the field of image processing), can decrease processing time up to 1,000 times for applications like medical imaging.[61]

Some example classes are search algorithms, sorting algorithms, merge algorithms, numerical algorithms, graph algorithms, string algorithms, computational geometric algorithms, combinatorial algorithms, medical algorithms, machine learning, cryptography, data compression algorithms and parsing techniques.

For example, dynamic programming was invented for optimization of resource consumption in industry, but is now used in solving a broad range of problems in many fields.

Owing to this, it was found to be more suitable to classify the problems themselves instead of the algorithms into equivalence classes based on the complexity of the best possible algorithms for them.

In the United States, a claim consisting solely of simple manipulations of abstract concepts, numbers, or signals does not constitute 'processes' (USPTO 2006), and hence algorithms are not patentable (as in Gottschalk v.

The patenting of software is highly controversial, and there are highly criticized patents involving algorithms, especially data compression algorithms, such as Unisys' LZW patent.

300 BC).[8]:Ch 9.1 Babylonian clay tablets describe and employ algorithmic procedures to compute the time and place of significant astronomical events.[70]

Tally-marks: To keep track of their flocks, their sacks of grain and their money the ancients used tallying: accumulating stones or marks scratched on sticks, or making discrete symbols in clay.

The work of the ancient Greek geometers (Euclidean algorithm), the Indian mathematician Brahmagupta, and the Persian mathematician Al-Khwarizmi (from whose name the terms 'algorism' and 'algorithm' are derived), and Western European mathematicians culminated in Leibniz's notion of the calculus ratiocinator (ca 1680):

good century and a half ahead of his time, Leibniz proposed an algebra of logic, an algebra that would specify the rules for manipulating logical concepts in the manner that ordinary algebra specifies the rules for manipulating numbers.[71]

led immediately to 'mechanical automata' beginning in the 13th century and finally to 'computational machines'—the difference engine and analytical engines of Charles Babbage and Countess Ada Lovelace, mid-19th century.[74]

Lovelace is credited with the first creation of an algorithm intended for processing on a computer – Babbage's analytical engine, the first device considered a real Turing-complete computer instead of just a calculator – and is sometimes called 'history's first programmer' as a result, though a full implementation of Babbage's second device would not be realized until decades after her lifetime.

Logical machines 1870—Stanley Jevons' 'logical abacus' and 'logical machine': The technical problem was to reduce Boolean equations when presented in a form similar to what are now known as Karnaugh maps.

Jevons (1880) describes first a simple 'abacus' of 'slips of wood furnished with pins, contrived so that any part or class of the [logical] combinations can be picked out mechanically .

More recently however I have reduced the system to a completely mechanical form, and have thus embodied the whole of the indirect process of inference in what may be called a Logical Machine' His machine came equipped with 'certain moveable wooden rods' and 'at the foot are 21 keys like those of a piano [etc] .

Another logician John Venn, however, in his 1881 Symbolic Logic, turned a jaundiced eye to this effort: 'I have no high estimate myself of the interest or importance of what are sometimes called logical machines ...

Jacquard loom, Hollerith punch cards, telegraphy and telephony—the electromechanical relay: Bell and Newell (1971) indicate that the Jacquard loom (1801), precursor to Hollerith cards (punch cards, 1887), and 'telephone switching technologies' were the roots of a tree leading to the development of the first computers.[78]

By the mid-19th century the telegraph, the precursor of the telephone, was in use throughout the world, its discrete and distinguishable encoding of letters as 'dots and dashes' a common sound.

Symbols and rules: In rapid succession the mathematics of George Boole (1847, 1854), Gottlob Frege (1879), and Giuseppe Peano (1888–1889) reduced arithmetic to a sequence of symbols manipulated by rules.

in which we see a ' 'formula language', that is a lingua characterica, a language written with special symbols, 'for pure thought', that is, free from rhetorical embellishments ...

The resultant considerations led to Kurt Gödel's paper (1931)—he specifically cites the paradox of the liar—that completely reduces rules of recursion to numbers.

Effective calculability: In an effort to solve the Entscheidungsproblem defined precisely by Hilbert in 1928, mathematicians first set about to define what was meant by an 'effective method' or 'effective calculation' or 'effective calculability' (i.e., a calculation that would succeed).

that the Entscheidungsproblem was unsolvable, Emil Post's definition of effective calculability as a worker mindlessly following a list of instructions to move left or right through a sequence of rooms and while there either mark or erase a paper or observe the paper and make a yes-no decision about the next instruction.[87]

Turing—his model of computation is now called a Turing machine—begins, as did Post, with an analysis of a human computer that he whittles down to a simple set of basic motions and 'states of mind'.

number of efforts have been directed toward further refinement of the definition of 'algorithm', and activity is on-going because of issues surrounding, in particular, foundations of mathematics (especially the Church–Turing thesis) and philosophy of mind (especially arguments about artificial intelligence).

Algorithm System Programmer

The algorithms in our eye tracking products consists of a complex combination of computer vision, signal processing and machine learning.

The hardware and mass market requirements for our wide range of products pose extremely high demands on the architecture and the quality of the source code.

We are looking for an experienced and driven system programmer to join our mission and tackle these challenges to bring our team and Tobii as a whole even further.

The Core team improves software architecture, does long term strategic development of the code and evangelizes development best practices.    We are looking for someone who has:  Here at Tobii we believe in team diversity, sustainable software development and great engineering practices and we hope that you feel the same.

Our technology brings a voice to people with speech impairments, it helps us understand human behavior and it is revolutionizing the way we interact with technology.

Who Really Controls What You See in Your Facebook Feed—and Why They Keep Changing It

Mosseri deputized product manager Max Eulenstein and user experience researcher Lauren Scissors to oversee the feed quality panel and ask it just those sorts of questions.

For instance, Eulenstein used the panel to test the hypothesis that the time a user spends looking at a story in her news feed might be a good indicator that she likes it, even if she didn’t actually click like.

Internet connections, which can make it seem like they’re spending a long time on a given story when they’re actually just waiting for the page to load.

'Fiction is outperforming reality': how YouTube's algorithm distorts truth

YouTube’s recommendation system draws on techniques in machine learning to decide which videos are auto-played or appear “up next”.

Aggregate data revealing which YouTube videos are heavily promoted by the algorithm, or how many views individual videos receive from “up next” suggestions, is also withheld from the public.

On most of those dates, the software was programmed to begin with five videos obtained through search, capture the first five recommended videos, and repeat the process five times.

But on a handful of dates, Chaslot tweaked his program, starting off with three or four search videos, capturing three or four layers of recommended videos, and repeating the process up to six times in a row.

Whichever combinations of searches, recommendations and repeats Chaslot used, the program was doing the same thing: detecting videos that YouTube was placing “up next” as enticing thumbnails on the right-hand side of the video player.

an example of a video deemed politically neutral or even-handed was this NBC News broadcast of the second presidential debate.) Many mainstream news clips, including ones from MSNBC, Fox and CNN, were judged to fall into the “even-handed” category, as were many mainstream comedy clips created by the likes of Saturday Night Live, John Oliver and Stephen Colbert.

After counting only those videos we could watch, we conducted a second analysis to include those missing videos whose titles strongly indicated the content would have been beneficial to one of the campaigns.

(This data was only partial, because it was not possible to identify channels behind missing videos.) Here are the top 10 channels, ranked in order of the number of “recommendations” Chaslot’s program detected.

“Over the months leading up to the election, these videos were clearly boosted by a vigorous, sustained social media campaign involving thousands of accounts controlled by political operatives, including a large number of bots,” said John Kelly, Graphika’s executive director.

“The most numerous and best-connected of these were Twitter accounts supporting President Trump’s campaign, but a very active minority included accounts focused on conspiracy theories, support for WikiLeaks, and official Russian outlets and alleged disinformation sources.” Kelly then looked specifically at which Twitter networks were pushing videos that we had categorised as beneficial to Trump or Clinton.

Far more of the links promoting Trump content were repeat citations by the same accounts, which is characteristic of automated amplification.” Finally, we shared with Graphika a subset of a dozen videos that were both highly recommended by YouTube, according to the above metrics, and particularly egregious examples of fake or divisive anti-Clinton video content.

The tweets promoting them almost always began after midnight the day of the video’s appearance on YouTube, typically between 1am and 4am EDT, an odd time of the night for US citizens to be first noticing videos.

“The sample of 8,000 videos they evaluated does not paint an accurate picture of what videos were recommended on YouTube over a year ago in the run-up to the US presidential election.” “Our search and recommendation systems reflect what people search for, the number of videos available, and the videos people choose to watch on YouTube,” the continued.

that is a reflection of viewer interest.” The spokesperson added: “Our only conclusion is that the Guardian is attempting to shoehorn research, data, and their incorrect conclusions into a common narrative about the role of technology in last year’s election.

The reality of how our systems work, however, simply doesn’t support that premise.” Last week, it emerged that the Senate intelligence committee wrote to Google demanding to know what the company was doing to prevent a “malign incursion” of YouTube’s recommendation algorithm – which the top-ranking Democrat on the committee had warned was “particularly susceptible to foreign influence”.

When people enter news-related search queries, we prominently display a ‘Top News’ shelf in their search results with relevant YouTube content from authoritative news sources.” It continued: “We also take a tough stance on videos that do not clearly violate our policies but contain inflammatory religious or supremacist content.

These videos are placed behind an warning interstitial, are not monetized, recommended or eligible for comments or user endorsements.” “We appreciate the Guardian’s work to shine a spotlight on this challenging issue,” YouTube added.

“We know there is more to do here and we’re looking forward to making more announcements in the months ahead.” The above research was conducted by Erin McCormick, a Berkeley-based investigative reporter and former San Francisco Chronicle database editor, and Paul Lewis, the Guardian’s west coast bureau chief and former Washington correspondent.

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