“cs is the only scientific discipline that cannot be defined in a single sentence”

• “CS is the only scientific discipline that cannot be defined in a single sentence”

• Applied science?

• Engineering discipline?

• Narrative of a transformative technology?

• (in the era of the Internet) Also a natural and social science?

Queen: Power, authority, pride, beauty

• Queen: Power, authority, pride, beauty

• Servant: Influences and transforms by being useful, powerful, and universal

• My point: CS is the new math

A bit is either 0 or 1

• A bit is either 0 or 1

• A qubit is in both states Q =  |0 +  |1

• An n-qubit system is in 2n states at the same time!

How curious, Nature is extravagant!

• How curious, Nature is extravagant!

• 2. Oh my God, how do you simulate such a system on a computer?

• But what if we built a computer

• out of these things?

Yes!

• Yes!

• No, because of a thousand annoying little problems and details (plus, eventually, lack of funding…)

• No, because Quantum Physics breaks down for large numbers of particles!!!

“Quantum computation is as much about testing Quantum Physics as it is about building powerful computers.”

• “Quantum computation is as much about testing Quantum Physics as it is about building powerful computers.”

They exist in two-player

• They exist in two-player

• zero-sum games,1928

• John Nash 1950:

• all games have one!

• “The Nash equilibrium lies at the foundations of modern economic thought.”

• Roger Myerson

• “Nobody would take seriously a solution concept that is empty for some games.”

• Roger Myerson

Finding

• Finding

• equilibria

• is an

• intractable

• problem!

• “If your laptop can’t find it, neither can the market”

• Kamal Jain

• New, more “algorithmic” solution concept based on player dynamics?

• “What algorithm could have created

• all this

• in a mere 1012 steps?”

Genetics (Mendel, 1866 – really, 1901)

• Genetics (Mendel, 1866 – really, 1901)

• The crisis (1901 – 1930)

• The synthesis through math (1930 – )

• The genomics revolution (1980 – )

• Why so much genetic diversity?

• What is the role of sex/recombination?

• Is Evolution optimizing something?

The Fisher-Wright-Haldane model is mathematically equivalent to a repeated game between genes:

• The Fisher-Wright-Haldane model is mathematically equivalent to a repeated game between genes:

• The strategies of each gene are its alleles

• The probabilities of play are the frequencies

• The common utility is the organism’s fitness

• The genes update their probabilities of play through the multiplicative updates algorithm!

(Also known as no-regret learning)

• (Also known as no-regret learning)

• A simple, common-sense algorithm known in CS for its surprising aptness at solving many sophisticated problems

• At each step, increase the weight of allele i by a factor of (1 + ε fi)

• fi is the allele’s fitness in the current environment created by the other genes

Convex optimization duality: Each gene “seeks to optimize” the sum of two quantities:

• Convex optimization duality: Each gene “seeks to optimize” the sum of two quantities:

• allele frequencies cumulative fitness

• maxx Φ(x) = H(x) + s F x
• entropy selection strength

• Why so much genetic diversity?

• What is the role of sex/recombination?

• Is Evolution optimizing something?

• “What algorithm could have done this in a mere 1012 steps?”

Babies vs computers

• Babies vs computers

• Clever algorithms vs what happens in cortex

• Understanding Brain anatomy and function vs understanding the emergence of the Mind

• How does one think computationally about the Brain?

(Current work with Santosh Vempala, and Wolfgang Maas and his group in Graz)

• (Current work with Santosh Vempala, and Wolfgang Maas and his group in Graz)

• What is the boundary between “subsymbolic” and “symbolic” brain processes?

• One candidate: Assemblies of excitatory neurons in MTL

Each “thing” is represented by an assembly of many neurons (~0,5% of all)

• Each “thing” is represented by an assembly of many neurons (~0,5% of all)

• Every time we are thinking of the “thing” all these neurons fire

• If two “things” become related, these assemblies are “JOINed:” some neurons fire on either “thing”

Can this interpretattion be predicted by a realistic mathematical model of neurons and synapses? (It is predicted in simulations)

• Can this interpretattion be predicted by a realistic mathematical model of neurons and synapses? (It is predicted in simulations)

• How about operations besides JOIN such as LINK or BIND (e.g., “kick” is a “verb”)?

• Can assemblies be the right conceptual interface between CS and Brain Science?

• Connection with language?

CS is worth teaching not only because it is (a) a fascinating and open-ended subject; (b) useful and in dmand; (c) as central and present in the world today as mass, energy, and life…

• CS is worth teaching not only because it is (a) a fascinating and open-ended subject; (b) useful and in dmand; (c) as central and present in the world today as mass, energy, and life…

• …but also because (just like math)

• (d) without a solid understanding of computation you are handicapped as a scientist