Tuesday, 8 March 2011


Artificial intelligence has taken a big leap forward: two roboticists (Lipson and Zagal), working at the University of Chile, Santiago, have created what they claim is the first robot to possess “metacognition” — a form of self-awareness which involves the ability to observe ones’ own thought processes and thus alter one’s behavior accordingly.
The starfish-like robot (which has but four legs) accomplished this mind-like feat by first possessing two brains, similar to how humans possess two brain hemispheres (left and right*). This provided the key to the automaton’s adaptability within a dynamic, and unpredictable, environment.
The double bot brain was engineered such that one ‘controller’ (i.e., one brain) was “rewarded” for pursuing blue dots of  light moving in random circular patterns, and avoiding running into moving red dots. The second brain, meanwhile, modeled how well the first brain did in achieving its goal.
But then, to determine if the bot had adaptive self-awareness, the researchers reversed the rules (red dots pursued, blue dots avoided) of the first brain’s mission. The second brain was able to adapt to this change by filtering sensory data to make red dots seem blue and blue dots seem red; the robot, in effect, reflected on its own “thoughts” about the world and modified its behavior (in the second brain), fairly rapidly, to reflect the new reality.
This achievement represents a significant advancement over earlier successes with AI machines in which a robot was able to model its own body plan and movements in its computer brain, make “guesses” as to which of its randomly selected body-plan models was responsible for the correct behavior (movement), and then eliminate all the unsuccessful models, thus exhibiting an “analogue” form of natural selection (see Bongard, Zykov, Lipson, 2006). **

TOPIO, a humanoid robot, played ping pong at Tokyo International Robot Exhibition (IREX) 2009.
The team is already moving beyond this apparent meta-cognition stage and is attempting to enabled a robot to develop what’s known as a ‘theory of mind’ – the ability to “know” and predict what another person (or robot) is thinking. In an early experiment, the team had one robot observe another robot moving in a semi-erratic manner (in a spiral pattern) in the direction of a light source. After a short while, the observer bot was able to predict the other’s movement so well that it was able to “lay a trap” for it.
Lipson believes this to be a form of “mind reading”. However, a critic might argue that this is more movement reading, than mind, and that it remains to be proven that the observer bot has any understanding of the other’s “mind”. A behavior (such as the second bot trapping the first) might simulate some form of awareness of another’s thought process, but can we say for sure that this is what is really happening?
One idea that might lend credence to this claim is if the observer bot had a language capacity that allowed it to express its awareness, or ‘theory of mind’. Nearly two decades ago, pioneering cognitive biologists Maturana and Varela posited “Language is the sin qua non of that experience called mind.”
And, achieving such a “languaging” capacity in not out of the question; a few years ago, a team of European roboticists created a community of robots that not only learned language, but soon learned to invent new words and to share these new words with the other robots in the community (see: Luc Steels, of the University of Brussels/SONY Computer Science Laboratory in Paris).
It is conceivable that a similarly equipped robot — also possessing the two-brain structure of Lipson’s robots — could observe itself thinking about thinking, and express this awareness through its own (meta) language. Hopefully, we will be able to understand what it is trying to express when and if it does.

A Pick and Place robot in a factory. You've come a long way droidy.
In a recent SciAm article on this topic, Lipson stated:
“Our holy grail is to give machines the same kind of self-awareness capabilities that humans have”
One other question that remains, then: Will the robot develop a more complex simulation/awareness of itself, and the world, as it learns and interacts with the world, as we do?
The four-legged, robot also exhibited another curious behavior: when one of its legs was removed (so that it had to relearn to walk) , it seemed to show signs of what is known as phantom limb syndrome, the sensation ta one still has a limb though it is in fact missing (this is common in people who have lost limbs in war or accidents). In humans, this syndrome represent a form of mental aberration or neurosis (perhaps even an hallucination). A robot acting in this way — holding a false notion of itself — may give scientists and AI engineers a glimpse into robot mental illness.
A robot with a mental illness or neurosis? Yes, this seem entirely likely given the following three theorems:
1] Neurosis is accompanied (and is perhaps a function of) acute self-awareness; the more self-aware, the more potentially neurotic one becomes.
2} Robots with advanced heuristics (enabled by multiple brains, self-simulators and sensor inputs) will inevitably develop advanced self-awareness, thus the greater potential for 1] above.
3] There is an ancient, magickal maxim: Like begets like. The creator is in the created (in Biblical terms: “God made man in his own image.”

What would Freud say about this form of attachment?
Mayhaps the ‘Age of Spiritual Machines‘ could become an ‘Age of Neurotic Machines‘ (or Psychotic Machines, depending on your view of humans), too. So then, f this is be the fate of  I, Robot, let’s do our droid druggs a favor and engineer a robo-shrink, or, at least, a good self-help program…and a love for Beethoven.

Monday, 7 March 2011

Managing Free Text Archives with Linguistic Semantics

Semantic natural language processing interprets the meaning of free text and enables users to find, mine and organize very large archives quickly and effectively. Linguistic semantic processing finds all and only the desired information because it determines meaning in context and maps synonym and hyponym relationships. It avoids assigning incorrect relationships because meaning is precisely determined. At the same time it makes all the desired connections exhaustively because it is backed by a massive lexicon and semantic map. The key scalably of Cognition's linguistic semantic processing is bottom-up interpretation of the text, finding the meaning of words and phrases in the local context one at a time. The technology has a semantic map and algorithms that interpret language linguistically rather statistically, so that the meaning of a given document is independently determined. As a result the methods scale to a theoretically unlimited number of documents. Linguistic semantic NLP is being deployed in many applications that facilitate rapid and accurate management of very large archives. . 1. Free auto categorization - Texts are categorized into an existing ontology or a special client-defined ontology according to the salient concepts in them. 2. Segregation by genre - The software determines which of a predetermined set of genres a documents falls into. In the legal domain, the genre set might be "contracts" and within "contracts", "employment contract", "services contract", etc., PPM, "pricing proposal", "mortgage agreement", and so on.
3. Conceptual foldering (or tagging) - Documents are placed in conceptual folders using conceptual Boolean expressions that cover all of the topics desired for the folders. This is especially useful in e-Discovery, where documents can be culled leaving only the relevant portion to be reviewed.
4. Intelligent search - The semantic search function retrieves almost all and only the desired documents. Very high precision is achieved by disambiguating words in context, and by phrasal reasoning. Very high recall is achieved by paraphrase and ontological reasoning.
5. Text Analytics - Calculating the frequency and salience of words, word senses, concepts and phrases in a document or document base lays bare its significant semantic content.
6. Sentiment analysis - With semantic processing, sentiments can be determined. Existing lexical resources identify the "pejorative" and "negative" words.
7. Language monitoring - In some situations such as child chat or email, certain types of language may need to be blocked. Linguistic semantic processing detects undesirable (or desirable) language as defined by administrators.

Kathleen Dahlgren has a Ph.D. in Linguistics and a Post-Doc in Computer Science from UCLA. She has worked and contributed publications in computational linguistics for over 20 years. Her publications cover topics in sense disambiguation, question-answering, relevance, coherence and anaphora resolution. Her book, Naive Semantics for Natural Language Understanding primarily treats a method for representing commonsense knowledge and lexical knowledge, and how this can be used in sense disambiguation and discourse reasoning. The software offered at Cognition Technologies is patented by Kathleen Dahlgren and Edward P. Stabler, Jr., and has been under development for a number of years, so that it now has a wide coverage semantic map of English.

Sunday, 6 March 2011

Reflections on Watson the Computer


By Sally Blount / Kellogg School of Management

The gap between human and artificial intelligence seems to be getting smaller... on Feb. 16, IBM's “Watson” computer outsmarted two Jeopardy champions.
A recent edition of TIME magazine explored our quest for human perfection and the rapidly emerging human-technology interface. And the current issue of Atlantic magazine reports the ever-closer results of the Turing Test—which determines whether a human or computer program can hold the most human-like conversation for five minutes.

As I read about these technological advancements, I can't help thinking that, if given a chance, I would love to have a chip planted in my brain that would help me remember names. I meet so many people every day from across our 60,000-person community of students, administrators, faculty, alumni and corporate partners. I would feel so much better and be more effective if, with a little help from technology, I could remember everybody's names every time I saw them.
But then I begin to wonder: With that chip implanted, would I become progressively worse at naturally remembering names? I'm not sure I like that idea. . . and then I can't help but think, what is being human about, anyway? Is it really about each of us trying to become more perfect,each in our own way, or is there some broader, less individually-focused aim?
Once we create computers and performance-enhanced humans that can outperform real humans (by 2045, as TIME predicts), will we have found jobs and eradicated poverty for the billion-plus among us who live on less than $2 a day? Will we have the infrastructure in place to provide every human on the planet with access to clean water and a warm bed? Will we have found deterrents to dramatically reduce, if not halt,the black market for sex trafficking? If the answer to these questions is “yes,” then these technological advancements will be of true value to humanity. But I have a terrible feeling that in 2045 the answers will still be a resounding “no.”
That's because there are some human limitations that technology is far from being equipped to fix. It can't overcome limitations that we ourselves don't know how to solve. One of our most glaring challenges is our collective inability to build effective organizations—organizations that consistently and reliably perform in a way that exemplifies the best of human performance and values. Each day's news reinforces this truth—in the Middle East, Washington,Mexico, and in corporate, government and religious headquarters around the world—as startling and saddening revelations emerge about flawed and corrupt organizations.
If we really want to change the world, we need to put more resources into studying and enhancing our shared human capabilities at building organizations—be they firms, government agencies or NGOs. There are many pressing questions:What are the barriers that deter us? Can we develop and use technology in ways that can counter these barriers? What political and social infrastructure do we need to support organization building? What individual-level skills are needed to equip organization-builders and change agents in established bureaucracies? How does leadership rhetoric help us on this road?
Until we become as good at building — and sustaining — effective organizations as we are good at computer programming, we will never realize our full human potential.

Semantic Technologies Bear Fruit In Spite of Development Challenges

In a conversation with BioInform, Ted Slater, head of knowledge management services at Merck and the CSHALS conference chair, described this year's meeting as "the strongest program" in the four years of its existence.
"Four years ago ... nobody [really] knew about [semantics]," Slater said. "Now we are at the point where we're talking about ... expanding the scope a little bit [and asking,] 'What else can we add into the mix to make it a more complete picture?'"
This year's conference began with a series of hands-on tutorials coordinated by Joanne Luciano, a research associate professor at Rensselaer Polytechnic Institute, that were intended to show how the technology can be used to address drug development needs.
During the tutorials, participants used semantic web tools to create mashups using data from the Linked Open Data cloud and semantic data that they created from raw datasets. Participants were shown how to load data into the subject-predicate-object data structure dubbed the "triple store;" query it using the semantic query language SPARQL; use inference to expand experimental knowledge; and build dynamic visualizations from their results.
Luciano told BioInform that this was the first year that CSHALS offered practical tutorials and the response from participants was mostly positive. Furthermore, the tutorials were made available for users in the RDF format so that “we were in real time, during the tutorial, able to run parallel tracks to meet all the needs of the tutorial participants,” she said.
While it's clear to proponents that semantic technology adds value to data, several speakers at the conference indicated that there is room for improvement and that much of the community remains unaware of the advantages that the semantic web offers.
For example, Lawrence Hunter, director of the computational bioscience program and the Center for Computational Pharmacology at the University of Colorado, pointed out that the field is still lacking good approaches to enable "reasoning" or, in other words, to figure out how "formal representations of data can get us places that simple search and retrieval wouldn’t have gotten us."
During his presentation, John Madden, an associate professor of Pathology at Duke University, highlighted several factors that need to be considered in efforts to "render" information contained in medical documents, such as laboratory reports, physician's progress notes, admission summaries, in the RDF format.
A major challenge for these efforts, he said, is that these documents contain a lot of "non-explicit information" that’s difficult to capture in RDF such as background medical domain knowledge; the purpose of the medical document and the intent of the author; "hedges and uncertainty"; and anaphoric references, which he defined as "candidate triples where it's unclear what the subject is."
Yet despite its complexities, many researchers are finding useful applications for the technology. For example, Christopher Baker of the University of New Brunswick described a prototype of a semantic framework for automated classification and annotation of lipids.
The framework is comprised of an ontology developed in OWL-DL that uses structural features of small molecules to describe lipid classes; and two federated semantic web services deployed within the SADI framework, one of which identifies relevant chemical "subgraphs" and a second that “assigns chemical entities to appropriate ontology classes.”
Other talks from academic research groups described an open source software package based on Drupal that can be used to build semantic repositories of genomics experiments and a semantics-enabled framework that would keep doctors abreast of new research developments.
Creating Uniformity
Semantic technologies are also finding their way into industry. Sherri Matis-Mitchell, principal informatics scientist at AstraZeneca, described the first version of the firm’s knowledgebase, called PharmaConnect, which was released last October and integrates internal and external data to provide connections between targets, pathways, compounds, and diseases.
Matis-Mitchell explained that the tool allows users to conduct queries across multiple information sources "using unified concepts and vocabularies." She said that the idea behind adopting semantic technologies at AstraZeneca was to shorten the drug discovery timeframe by bringing in "knowledge to support decision-making" earlier on in the development process.
The knowledgebase is built on a system called Cortex and receives data from four workstreams. The first is chemistry intelligence, which supports specific business questions and can be used to create queries for compound names and structures. The second is competitive intelligence, which provides information about competing firms' drug-development efforts, while the final two streams are disease intelligence, used to assess drug targets; and drug safety intelligence.
In a separate presentation, Therese Vachon, head of the text mining services group at the Novartis Institutes for Biomedical Research, described the process of developing a federated layer to connect information stored in multiple data silos based on "controlled terminologies" that provide "uniform wording within and across data repositories."
Is the Tide Turning?
At last year's CSHALS, there was some suggestion that pharma's adoption of semantic methods was facing the roadblocks of tightening budgets, workforce cuts, and skepticism about the return on investment for these technologies (BI 03/05/2010)
Matis-Mitchell noted in an email to BioInform that generally new technologies take time to become widely accepted and that knowledge engineering and semantic technologies are no different.
She said her team overcomes this reluctance by regularly publishing its "successes to engender greater adoption of the tools and methods." While she could not provide additional details about these successes in the case of PharmaConnect for proprietaty reasons, she noted that the "main theme" is that it "helped to save time and resources and supported more efficient decision making."
However some vendors now feel that drug developers may be willing to give semantic tools a shot and are gearing up to provide products that support the technology.
In one presentation, Dexter Pratt, vice president of innovation and knowledge at Selventa, presented the company's Biological Expression Language, or BEL, a knowledge representation language that represents scientific findings as causal relationships that can be annotated with information about biological context, experimental methods, literature sources, and the curation process.
Pratt said that Selventa plans to release BEL as an open source language in the third quarter of this year and that it will be firm's first offering for the community.
Following his presentation, Pratt told BioInform that offering the tool under an open source license is "consistent" with Selventa's revised strategy, announced last December, when it changed its name from Genstruct and decided to emphasize its role as a data analysis partner for drug developers (BI 12/03/2010).
To help achieve this vision Selventa "will make the BEL Framework available to the community to promote the publishing of biological knowledge in a form that is use-neutral, open, and computable" Pratt said .adding that the company's pharma partners have been "extremely supportive" of the move.
Although the language has already been implemented in the Genstruct Technology Platform for eight years, In preparation for it's official release in the open source space, Selventa's developers are working to develop a "new build" of the legacy infrastructure that's " formalized, revised, and streamlined."

Friday, 4 February 2011

New Life for Semantic Technologies

Cambridge Semantics provides flexible solutions for the data deluge.

By Kevin Davies
February 4, 2011 | A small software company formed by a group of former IBM staffers is breathing new life into semantic technologies. But don’t look for Cambridge Semantics to harp on the term.
“The world of people well versed in semantic technology is still quite small,” says co-founder Lee Feigenbaum. “It’s important that anyone working with our software should not be IT. You won’t see the word ‘semantics’ anywhere in our software. It’s an enabler for us. We can’t build our software without these technologies, but now we’ve built them, we’ve no interest in preaching that you’re using semantics.” (see, “Masters of the Semantic Web,” Bio•IT World, Oct 2005)
“We don’t lead with ‘Semantic Web’ as a marketing term,” adds senior product manager Rob Gonzalez. “We’d like to see more companies like us trying to solve real-world problems. For us it’s about the problems we’re solving.”
Along with CTO Sean Martin, Feigenbaum was one of a group of about 20 people in an advanced technology group at IBM dating back to 1995. The group’s mission was to research new Internet technologies (including semantic technologies) and potential applications for IBM. An early client was a group of cancer researchers at the Massachusetts General Hospital (the Center for the Development of a Virtual Tumor), for which the IBM team helped to deploy semantic technologies for building and sharing models, data, and literature.
In 2007, Martin and Feigenbaum, together with Simon Martin and Emmett Eldred, established Cambridge Semantics and spent a couple of years building up the engineering team and testing early products before launching its first commercial product in late 2009. Luckily, much of the IBM group’s technology was open source. “People have been [saying] that they can’t build libraries or services that are really reusable or discoverable. We think with semantics, you get these benefits,” says Feigenbaum.
Early customers include Johnson & Johnson, Merck, and Biogen Idec, although Cambridge Semantics’ client base includes Fortune 500 companies in advertizing and the oil industry. “This technology can be used in many industries, but is particularly geared toward life sciences,” says Gonzalez. “The data bonanza isn’t comparable to other industries. Life scientists simply need this flexibility.”
Semantic Sidestep
There’s a saying that Feigenbaum admits is neither new nor particularly funny, but it makes a point: If you put ten Semantic Web advocates in a room, you’ll get 15 different explanations of what the Semantic Web is. “You have a loosely coupled set of technologies that people can use for a million different things. People will latch onto something and say this is the real semantic technology.”
Indeed, Feigenbaum is blunt in his criticism of vendors and users alike who proclaim the magical properties of the Semantic Web. “I’ve seen pharma talk about semantics as the ultimate data integration/analysis tool. That’s all well and good and we might get there in the next 10-15 years, but it’s never been what we’ve seen in semantics.”
For Feigenbaum, the interesting bit of semantic technology is the notion of rebranding data in a flexible and agile way. “The underlying properties of semantic technologies let you build very agile, adaptive software systems as data sources changes. It happens in all industries but especially in life sciences.”
Semantics is about flexibility and having a common data model upon which one can take information from a variety of sources—XML, relational databases, or public clinical trial database—and “map them to a common format not constrained by any a priori database schema or XML structure. We saw this flexibility in 2001, and proved it out at IBM. That’s what we wanted to leverage.”
Cambridge Semantics released its first three products in 2009. “There’s no magic to the software,” says Feigenbaum. Just an easy-to-use interface and set of tools that allows users to point to a particular area in a spreadsheet, for example, and ascribe a meaning, e.g. adverse event, assay result. “You have these common vocabularies and data models, and the system takes care of finding values that match and links them together, without having necessarily considered that way of linking things when you set up the system.”
The Anzo Data Collaboration Server, which sits on the user’s server, is semantic middleware, the plumbing that runs and connects everything else. Says Feigenbaum: “It invokes Web services. It has data services and server services that let you build flexible applications.”
Anzo on the Web is a Web 2.0-style application for self-service reporting of any data connected to the data collaboration server. Typically, when users want to use a new data source, Gonzalez explains, they have to change the database, then the application code, then the web tier. “With Anzo on the Web, you can bring the new data source easily into the data collaboration server, and it propagates throughout the system without requiring a lot of manual changes, so it’s resilient to new types of information being added.” The application is designed for scientists who aren’t necessarily IT experts. “They don’t have to go to IT to build new views; they can do it,” says Feigenbaum.
Anzo for Excel is a plug-in to Microsoft Excel that lets people use spreadsheets more effectively. It makes the collection of ad hoc data trivial, says Feigenbaum. “It turns Excel into a data collection application and lets it serve as a user interface for all this data integrated on the server. Now you can consume the data.” A recently-released second version adds an unnamed component that allows users to collect and integrate data from relational databases.
The company announced in mid-January an agreement with Cray to collectively develop and market solutions, including the Cray XMT system and the Anzo product suite. But Feigenbaum is also using the Amazon Cloud, particularly with new prospects. “The data integration paradigm we’re preaching is anathema to a lot of traditional IT,” says Feigenbaum, particularly in regard to procuring hardware, which can sometimes take months. “Many customers run a proof-of-concept in the Cloud with hosted versions of the software. That lets them prove out the technology and work on the procurement to deploy inside their firewall.”
One of the chief benefits of Cambridge Semantics, says Feigenbaum, is that it affords pharma customers the ability not only to pull in and analyze the data from a traditional database but also “the last 10-15% of their data that might be lurking in a desktop spreadsheet or a public resource such as NCBI. They don’t want to spend millions of dollars and 18 months only to get 90% of the way. They need to handle the heterogeneity of Excel and public data. [The missing data] might only be a small part of the total information but it’s a deal breaker.”
Early users span applications from manufacturing quality control to budgeting, allowing customers such as Biogen Idec to compare their actual spend with budget projections. Merck is using Cambridge Semantics applications to procure time on lab equipment.
Cambridge Semantics is still learning from its early customers where its technology can be leveraged. One promising area is in clinical trial data management. Says Feigenbaum: “When you’ve brought together data that don’t normally talk to each other, there’s a bunch of things you can do, such as looking at data for a drug across trials/phases. But some [historical] trials might have used SAS or Oracle Clinical. This is a good way to bring data together,” perhaps to identify reporting discrepancies for regulatory purposes.
An alternative term for semantic technologies that is growing in popularity is “linked data.” “It’s fine,” shrugs Feigenbaum. “It’s just another name. It’s had some success in life sciences, but I don’t care what it’s called.” •

Thursday, 3 February 2011

An Immortal Lesson in Design

The Mac's Inventor's Deathbed Gift: An Immortal Lesson in Design For His Son

The man who created the Mac interface gives his son Aza Raskin a final gift that that testifies to the beauty and power of simplicity.


Twenty five days before my father Jef died, on my birthday exactly six years ago, he gave me a present. He had the sparkle back in his eye -- the one that had been reduced by pancreatic cancer to an ashen ember -- when he gave it to me. It was a small package, rectangular in shape, in crisp brown-paper wrapping. Twine neatly wrapped around the corners, crisscrossing back and forth arriving at a bow crafted by the sure hands of a man who built his first model airplane at age seven.
This small brown package was to be the final gift my father ever gave me.
My family does gifts strangely. For instance, we have our own mangled interpretation of Hanukkah, where each person of the family has a night to give out presents. If we have five people home for Hanukkah, we celebrate only five of the eight nights. The joy of gifts are in the giving, not receiving, so before opening your present you must first guess what’s inside. This tradition is "plenty questions," a more forgiving version than the standard twenty questions.
“Animal, vegetable, or mineral?”, I ask.
I stare at the package. In it is my father. The man who invented the Mac.
We are in it for the game of teasing the gift out of the gifter. It's like extracting a ball of yarn from a kitten. The tugs, pulls, and misdirections are the fun. The question must answerable by a simple "yes" or "no." Naturally, the later into the questions we get, the more liberal this rule becomes. We don't break the rule exactly, but answers become a series of "not-exactly"s and "yes-but"s. In past years, the givers have often spent hours creating elaborate disguises for the gifts. I've shaped styrofoam into a fantastic reptilian shape to disguise a pair of earrings for my mother. She guessed them perfectly anyway. There may be collusion going on.
"Mineral," my father says.
We often waste questions on silly asides. We ask about refrigerators and ostrich eggs when the gift is clearly book shaped. But my father is sick. Where there was once the thought that a cure might be found, only fleeting misplaced hope remains like a high school summer fling dissipating in the face of college. We know there isn't much time. Still I ask.
I stare at the package in my hands. In it is my father. The man who invented the Macintosh and misnamed what should be "typefaces" as the "fonts" menu. He never forgave himself for his incorrect usage of English. He groomed me to use language exactingly and considered that mistake a failure of being young and reckless with semantics. The man who invented click-and-drag was now the man who could hardly keep his gaze focused on his son. The box is, of course, smaller than a bread box. It's a question we always ask. My family smiles only out of habit.
"No," my father says. A long pause. "No," he says again, "it is smaller than a bread box. Smaller and sharper." He speeds the guessing game along. Time.
The gift was a message about an entire way of thought.
"Sharper?" I ask. A knife? The box is too small for a typical kitchen knife. It could be a Swiss Army knife. Jef always carries one. The big blade is for food, the little blade for everything else. He gets a bit indignant if you borrow it and use the wrong blade. I have a Swiss Army knife, but I haven't carried it since airport security theater ramped up after 9/11. It probably isn't a knife. Maybe a razor? One can't just ask outright, that doesn't give enough information when you are wrong. Something sharp could be many things. Seeking something more strategic I ask, "Can it be found in a bathroom?"
Long pause.
"Yes."
Three days before he passed, Jef had an accident. He needed to use the restroom, so -- stooped under his arm -- I supported his weight as he hobble to his business. There was something quietly unsettling about escorting my father to a toilet that had been taller than me when we first moved into the house twenty years earlier. I sat him down, walked out, and closed the door. Moments later, a crash jolted the house. I slammed the door open. The metallic smell of water fresh from a pipe whipped my nose and water flooded the floor. The toilet was dislocated from its base like an arm from its socket, and lodged between the toilet and the wall was my father. Despite his size, he looked small and meager. He stared up at me with eyes full of innocent surprise. Why am I on the floor, they asked? Why am I wet? The shocked curiosity in his wide-open eyes is the single most haunting image I have of my father. In the dark space between closing my eyes and falling asleep, that image sometimes steals in and taints me. When it does, there is no help for it. I have to get out of bed and go for a run. Otherwise, sleep will be overshadowed by those confused, guileless eyes.
"It must be a razor?" I ask. He nods his assent with a satisfied smile. He gestures for me to open it. Carefully undoing the knot, the twine, and the paper reveals a cardboard box on which he has written "For Pogonotomy." Of course there is a word for beard trimming, and of course my father knows and uses it. In high school, I played a trick on my teachers: in every essay I used my own made-up word. I used "indelic" to mean something between "endemic" and "inextricably entwined." No matter how many times I trotted it out, not one of my teachers caught me. I used it once in passing with my father and he immediately but gently pointed it out as a non-word. Some men spend time meticulously trimming their beard. My father trimmed his vocabulary. Language is communication, and my father was fastidious about it. Often when we got into particularly deep conversations, he'd pause and continue the rest of the discussion in written form where he could distill his thoughts into a sharp crystalline relief.
The razor itself was a vintage safety razor. Looking at it, I understood the allure. It is an inventive and simple design. The razor takes a flat blade and arches it under a metal shield, giving the blade both greater mechanical strength as well as a protective sheath that keeps you safe. It's the kind of clear insight for which all designers and inventors strive: beauty in turning constraints into advantages.
That razor is a message, rendered in steel and wood, about an incorporeal way of thought. That was my father's final gift to me: A way of looking at the world through the lens of playful questioning, which reveals more than just an answer.Twenty five days later, the razor remained but my father did not.
Jef, I miss you.

Monday, 31 January 2011

Artificial intelligence based on Darwin’s idea

There are too few ethicists contemplating scenarios of a future populated by out-of-control, “differently sentient’’ beings — a job science fiction writers have been doing for generations.

But when machines start thinking for themselves, there are no guarantees robots will be thinking about anyone else but themselves.

Indeed, consider the robots at the University of Vermont that have already begun to evolve.
In an engineering first, and using the same processes of natural selection that made humans so clever, UVM roboticist Josh Bongard has created robots — both real and simulated — whose body shapes change as they learn to walk.

www.youtube.com/watch?v=ckwsvmf3slU

Bongard said that like animals, his robots’ artificial brains evolved not in isolation, but in conjunction with their changing bodies and individual environmental challenges.
The evolving robots in Bongard’s experiment started with only a few moving parts, like tadpoles. But over time, they became creatures with four legs that walked faster, and with steadier gaits than those that were stuck with “fixed body forms’’ (as demonstrated by their responses to being knocked with a stick, for example), according to a UVM announcement.
The robots exist inside a computer program that looks like a 3-D video game, and as prototypes made from Lego kits.
Bongard expects that by using evolutionary processes, engineers will make smarter and more efficient robots, capable of cleaning up construction sites and maintaining roads.
I expect we will see more sophisticated personal robots coming out of evolutionary robotics, as well, including ones that can walk dogs or serve as companions to the elderly.
Research

Softer components make robots safer to be around

Nothing seems quite so chilling as the thought of being touched by robotic hands.
I am of course thinking of the robotic hands of fiction, such as Robby’s pincers on “Forbidden Planet.’’
Now scientists are developing softer hands for the machines that in the future will perform delicate tasks.
Scientists working with The Whitesides Research Group (gmwgroup.harvard.edu/) at Harvard University recently developed a soft plastic gripper for a robot, one that can grasp a raw egg without cracking it or hold a mouse without crushing the wee creature.
The starfish-shaped grippers are embedded with elastic plastic channels that when inflated with air, expand in those areas that are the most yielding.
The result is a grip that is firm enough to lift a fragile object, but which requires none of the programming that a robot with a hard mechanical hands needs to avoid clamping down too hard on an object.
A robot with a pair of the Whitesides starfish-shaped mitts may not be as attractive as Maria, the charismatic female robot in the 1927 classic film “Metropolis,’’ but it would be capable of holding flowers without crushing them or gripping the arm of a patient without breaking bones and bruising flesh.
Robots with a lighter touch might even make good surgeons one day